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Analyzing data that possess some form of nesting is often challenging for applied researchers or district staff who are involved in or in charge of conducting data analyses. This report provides a description of the challenges for analyzing nested data and provides a primer of how multilevel regression modeling may be used to resolve these challenges. An illustration from the companion report, The correlates of academic performance for English language learner students in a New England district (REL 2014-020), is provided to show how multilevel modeling procedures are used and how the results are interpreted. "Step by step procedure for using the Advanced Statistics module of SPSS IBM Statistics" is appended.

Multilevel models (MLMs) have proven themselves to be very useful in social science research, as data from a variety of sources is sampled such that individuals at level-1 are nested within clusters such as schools, hospitals, counseling centers, and business entities at level-2. MLMs using restricted maximum likelihood estimation (REML) provide researchers with accurate estimates of parameters and standard errors at all levels of the data when the assumption of normality is met, and outliers are not present in the sample. However, if outliers at either levels 1 or 2 occur, the parameter estimates and standard errors produced by REML can both be compromised. Two estimation approaches for use when outliers are present have been proposed recently in the literature. Although the two methods, one based on ranks and the other on heavy tailed distributions of model errors, show promise, neither has heretofore been studied comprehensively across a wide variety of data conditions, nor have they been compared with one another. Thus, the purpose of the current study was to compare the rank and heavy tailed based estimation techniques with one another, and with REML, in terms of their ability to estimate level-1 fixed effects, under a variety of data conditions. Results of the study revealed that the rank based and heavy tailed method provide less biased estimates than REML when outliers are present, and that the rank approaches yield smaller standard errors than the heavy tailed approach in the presence of outliers. Implications of these results are discussed.

This study introduces three growth modeling techniques: latent growth modeling (LGM), hierarchical linear modeling (HLM), and longitudinal profile analysis via multidimensional scaling (LPAMS). It compares the multilevel growth parameter estimates and potential predictor effects obtained using LGM, HLM, and LPAMS. The purpose of this multilevel growth analysis is to alert applied researchers to selected analytical issues that are required for consideration in decisions to apply one of these three approaches to longitudinal academic achievement studies. The results indicated that there were no significant distinctions on either mean growth parameter estimates or on the effects of potential predictors to growth factors at both the student and school levels. However, the study also produced equivocal findings on the statistical testing of variance and covariance growth parameter estimates. Other practical issues pertaining to the three growth modeling methods are also discussed. (Contains 4 tables, 1 figure and 4 notes.)

In this paper, to improve power factor a simple model based on five levels multiple pole is used. It is also used to improve harmonic distortion and efficiency which is done by using reduced number of component counts. Most of the work has been done to obtain these factors. In this project, 5L-M2UPFR is used which give almost unity power factor. By this method, the unity power factor with input current shaping is obtained with less number of measurement components. This paper uses balanced and unbalanced load condition over which these improved response is obtained. Ranjan Kumar Rai | Umashankar Patel"An Analysis and Modeling of Grid Connected Multiple-Pole Multilevel Unity Power Factor Rectifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7137.pdf Article URL: http://www.ijtsrd.com/engineering/electrical-engineering/7137/an-analysis-and-modeling-of-grid-connected-multiple-pole-multilevel-unity-power-factor-rectifier/ranjan-kumar-rai

The field of color categorization has always been intrinsically multi- and inter-disciplinary, since its beginnings in the nineteenth century. The main contribution of this book is to foster a new level of integration among different approaches to the anthropological study of color. The editors have put great effort into bringing together research from anthropology, linguistics, psychology, semiotics, and a variety of other fields, by promoting the exploration of the different but interacting and complementary ways in which these various perspectives model the domain of color experience. By so doing, they significantly promote the emergence of a coherent field of the anthropology of color.

The field of color categorization has always been intrinsically multi- and inter-disciplinary, since its beginnings in the nineteenth century. The main contribution of this book is to foster a new level of integration among different approaches to the anthropological study of color. The editors have put great effort into bringing together research from anthropology, linguistics, psychology, semiotics, and a variety of other fields, by promoting the exploration of the different but interacting and complementary ways in which these various perspectives model the domain of color experience. By so doing, they significantly promote the emergence of a coherent field of the anthropology of color.

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM; Cai, 2008, 2010a, 2010b). Results indicate that the MH-RM algorithm can produce estimates and standard errors efficiently. Simulations, with various sampling and measurement structure conditions, were conducted to obtain information about the performance of nonlinear multilevel latent variable modeling compared to traditional hierarchical linear modeling. Results suggest that nonlinear multilevel latent variable modeling can more properly estimate and detect contextual effects than the traditional approach. As an empirical illustration, data from the Programme for International Student Assessment (PISA; OECD, 2000) were analyzed. [This paper was published in "Journal of Educational and Behavioral Statistics" v39 n6 p550-582 2014 (EJ1048233).]

This study seeks to demonstrate a method for treatment effect estimation in a multisite observational study where the treatment is highly selective and the assignment mechanism varies across sites. The method is demonstrated by addressing three primary research questions about the effect of 8th grade algebra: (1) For students who take algebra in 8th grade, what is the average effect of taking algebra in 8th grade on algebra achievement by the end of 9th grade?; (2) Does the average effect vary across students with different levels of demonstrated 7th grade mathematics achievement and propensity for taking 8th grade algebra?; and (3) Does the average effect vary across classrooms and schools? Through these three research questions, the author focuses on preprocessing the data with propensity score matching and imputation of the counterfactual, and on the exploration of treatment effect heterogeneity with multilevel modeling. This paper recognizes, but does not directly address, the importance of sensitivity analysis. (Contains 3 tables and 2 figures.)

The anchoring and adjustment effect was first defined by Tversky and Kahneman (1974) as a disproportionate influence on decision-makers, leading them to produce judgments biased toward an initially presented value. In their study, participants were asked to estimate the percentage of African countries in the United Nations after spinning a wheel of fortune that generated a random number between 0 and 100. First, participants considered whether the actual percentage was higher or lower than the number on the wheel. Then, they provided their own numerical estimate. The findings revealed a significant correlation between the random anchor values and participants’ final judgments. The anchoring and adjustment effect involves two distinct stages: anchoring, where an initial reference value is established, and adjustment, where the final estimate is modified upward or downward in relation to the anchor. Following this foundational study, the anchoring and adjustment effect has been demonstrated across a wide range of domains. For instance, researchers have examined questions such as the boiling point of water on Mount Everest and the number of days it takes Mars to orbit the sun (Epley & Gilovich, 2001), the exact length of the Mississippi River (McElroy & Dowd, 2007), and probabilistic estimates such as the likelihood of a nuclear war (Plous, 1989). The anchoring and adjustment effect has been explained as a mental shortcut in which individuals cease adjusting the reference value—the "anchor"—once they reach a value that appears subjectively reasonable or sufficiently close (Epley & Gilovich, 2006). This explanation aligns with the concept of satisficing behavior in surveys, which refers to suboptimal mental effort exerted during survey completion (Krosnick, 1991). Therefore, when adjacent items in a survey are similar, respondents may begin selecting the same response option as they did for the previous item (Chapman & Johnson, 1999; Gehlbach & Barge, 2012; Strack & Mussweiler, 1997). This behavior is consistent with the notion of assimilation effects, in which responses to items become increasingly similar (Dillman et al., 2009). To model the anchoring effect in surveys, we suggest the use of autoregression (AR) methods of longitudinal data, modeling the response to each item as a function of its serial position – that is, the time it was answered. These methods have been developed to examine whether a value measured for a particular user at one point in time affects the same value at the following point in time (Hox et al., 2018, p. 1-2), utilizing multilevel models (MLMs). These models refer to nested data models. When data are nested within respondents across time, MLM typically assumes variance uniformity, which requires that all the population variance of the repeated measures and all the population covariance of the measures be equal. This model is called compound symmetry (CS). However, in models that analyze successive events, it is necessary to examine AR effects (Hox et al., 2018, p. 76). We will compare two MLM approaches to modeling variances and covariances: compound symmetry (CS) and autoregression (AR). We assume that AR demonstrates an anchoring bias, as we see a correlation that is unrelated to content and is stronger between close questions than distant questions. Using AR, and comparing it to CS, we can estimate the size of the anchoring bias in the survey. In addition, we can estimate the effect of modeling AR on the validity of predictors of interest. If modeling AR changes the validity estimates, AR modeling can be used to neutralize the anchoring effect on data validity.

This project will use a multilevel modeling framework originally proposed in LeBoeuf et al., (2023) to investigate whether racial disparities in suspensions in the US grew or shrank between 2011 and 2018, as well as whether specific school characteristics are associated with higher suspension rates in a school. In a separate analysis looking at a single year of data, we will also consider whether racial disparities in suspensions vary as a function of the racial diversity of the school.

The study is designed to demonstrate and test the utility of the proposed two-stage matching method compared to other analytic methods traditionally employed for multisite observational studies. More specifically, the study addresses the following research questions: (1) How do different specifications of the matching method influence covariate balance? (2) How do different specifications in the matching method influence inferences about treatment effect and effect heterogeneity? The different matching method specifications include differences in the propensity score model and whether a between-site match, within-site match, or two-stage matching process is used. The simulation results indicate that the two-stage matching method balances the desire for within-site covariate balance and the desire to retain as many treatment units in the analysis as possible. Relative to more straightforward matching methods, however, the two-stage matching method does not result in greater covariate balance nor less biased effect estimation. As a result, more straightforward methods that address the nested data structure--such as within-site matching or pooled matching with a random-intercept-and-slope propensity score model--might be preferable to the more complex two-stage matching method. These conclusions are based on a finite set of data generating conditions, with a small set of important confounders at both the unit and site level and a reasonable within-site sample size for matching. Future research should examine the performance of various propensity score model and matching methods under more extreme data conditions. (Contains 2 tables and 5 figures.)

The study is designed to demonstrate and test the utility of the proposed two-stage matching method compared to other analytic methods traditionally employed for multisite observational studies. More specifically, the study addresses the following research questions: (1) How do different specifications of the matching method influence covariate balance? (2) How do different specifications in the matching method influence inferences about treatment effect and effect heterogeneity? The different matching method specifications include differences in the propensity score model and whether a between-site match, within-site match, or two-stage matching process is used. The simulation results indicate that the two-stage matching method balances the desire for within-site covariate balance and the desire to retain as many treatment units in the analysis as possible. Relative to more straightforward matching methods, however, the two-stage matching method does not result in greater covariate balance nor less biased effect estimation. As a result, more straightforward methods that address the nested data structure--such as within-site matching or pooled matching with a random-intercept-and-slope propensity score model--might be preferable to the more complex two-stage matching method. These conclusions are based on a finite set of data generating conditions, with a small set of important confounders at both the unit and site level and a reasonable within-site sample size for matching. Future research should examine the performance of various propensity score model and matching methods under more extreme data conditions. (Contains 2 tables and 5 figures.)

This paper proposes a new optimal high level multilevel inverter with minimum number of components. This multi level inverter (MLI) is designed with series combination of basic units which can generate positive levels at output. DC source values applied for each basic unit is different with another. An H bridge is connected across proposed MLI for generating negative levels along with positive levels at output and that inverter considered as proposed high level optimal multilevel inverter. Single unit is responsible producing 21 levels. Therefore six units are connected in cascaded form to increase number of levels as 127 at output. Decrease in the number of power switches, driver circuits, and dc voltage sources are the improvement of the proposed MLI. Sinusoidal multiple pulse width modulation (SPWM) technique is implemented to produce pulses for turning ON switches according requirement. Low total harmonic distortion at output voltage or current production is major advantage of proposed module. The validations of proposed MLI results are verified through MATLAB/SIMULINK.

Traditional approaches to the investigation of the objectivity of ratings for constructed-response items are based on classical test theory, which is item-dependent and sample-dependent. Item response theory overcomes this drawback by decomposing item difficulties into genuine difficulties and rater severity. In so doing, objectivity of ability estimates is achieved, even though objectivity of ratings is poor. However, most item response models are too rigid to fit complexity of rater severities. Also, other types of items in the same test are excluded when estimating rater severities. These problems are addressed in this study. Several advanced models are proposed to explore severity changes over items and within items. In addition, multilevel and multidimensional models are formed to incorporate both multiple-choice items and constructed-response items in the test to increase estimating accuracy and model fit. The proposed models are made possible by a newly developed item response model, the multidimensional and multilevel random coefficients multinomial logit model. A real data set from the biology subject of the 1995 Joint College Entrance Examination in Taiwan was analyzed to demonstrate the advantages of this approach. (Contains 5 tables, 6 figures, and 35 references.) (Author)

This article is from BMC Proceedings , volume 8 . Abstract Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.

This article is from BMC Proceedings , volume 8 . Abstract Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.

Major Depressive Disorder (MDD) affects more than 300 million people in the world and shows an increasing trend in prevalence (World Health Organization, 2017). Following the first episode, about 15-35% of patients with MDD develop recurrent episodes within the first years (Bukh et al., 2016). Less than half of all patients with MDD remain symptom-free for two years after recovery (Kanai et al., 2003). Moreover, the number of lifetime episodes, severity of preceding episode and presence of subclinical residual symptoms have been identified as risk factors to experience further recurrent episodes (Keller & Boland, 1998; Kennedy et al., 2003; Pettit et al., 2006). Taken together, these factors contribute to the accumulation of disease burden and the development of long-term chronicity of MDD (Hardeveld et al., 2013). Structural neuroimaging techniques may contribute to our understanding of the underlying neural mechanisms of reoccurrence in MDD (Kang & Cho, 2020). Subsequently, this could facilitate relapse prognosis and potentially advance maintenance treatment. Cross-sectional neuroimaging studies, including meta-analyses from international consortia (e.g., ENIGMA), suggest brain structural differences between MDD patients and healthy controls (HC; Gray et al., 2020; Schmaal et al., 2016, 2017), whereas effect sizes are small (Winter et al., 2022). Reductions in gray matter volume (GMV) and cortical thickness in brain areas such as the hippocampus (Campbell et al., 2004; Schmaal et al., 2016; McKinnon et al., 2009), insula (Lai & Wu, 2014; Stratmann et al., 2014; H. Zhang et al., 2016) and the prefrontal cortex (Bora et al., 2012; Schmaal et al., 2017; Zhang et al., 2018) are reported most frequently in association with MDD. These morphometric changes seem to be associated with the course of disease, specifically the number of recurrent episodes and duration of illness (Lemke, Romankiewicz, et al., 2022; McKinnon et al., 2009; Stratmann et al., 2014; Treadway et al., 2015). However, cross-sectional studies are restricted to correlative statements and fail to explain the direct interplay between recurrence of MDD and neural changes. Longitudinal studies in larger, well-characterized samples are needed to classify these changes into risk factors, correlates of the acute depressive state and consequences of prior depressive episodes. In previous longitudinal studies, brain structural alterations in regions, such as the dorsolateral prefrontal cortex (DLPFC), insula, hippocampus, and anterior cingulate cortex were observed (Dohm et al., 2017). Studies have reported a greater decline in GMV in these regions in non-remitters compared to patients whose MDD was in remission at follow-up assessment (Frodl et al., 2008; Phillips et al., 2015; Taylor et al., 2014). Vice versa, some studies reported an increase of GMV and cortical thickness in these regions with achieved remission (Hou et al., 2012; Phillips et al., 2015; Zaremba et al., 2018). Nonetheless, longitudinal research focusing exclusively on remission status at follow-up neglect the course of illness between scans, which is essential for accessing the link between brain alterations and relapse. A few studies investigated morphological changes as a function of relapse during follow-up interval (Frodl et al., 2008; Soriano-Mas et al., 2011), while some additionally controlled for confounding variables such as psychopharmacological treatment (Lemke, Klute, et al., 2022; Zaremba et al., 2018). These studies found that depressive relapse, as a distinct marker of disease progression during the interscan interval, is specifically linked to decline of GMV and cortical thickness and surface area in the insula and DLPFC. A loss of GMV in the insula and hippocampus has further been demonstrated in patients with severe courses of affective disorders, characterized by a hospitalization during a nine-year follow-up (Förster et al., 2023). Taken together, the findings provide first evidence of the negative impact of disease progression on the morphology of these brain regions. However, the studies share two key limitations. Firstly, grouping in dependence of experiencing at least one relapse does not account for variations in length of depressive episodes and thus disregards the duration the depressive state might affect the brain. Secondly, the majority of longitudinal imaging studies are restricted to two time points which frame a follow-up interval and used statistical models that evaluated the effect of relapse by comparing group means (Dohm et al., 2017; Lemke, Klute, et al., 2022; Zaremba et al., 2018). Given the broad heterogeneity of MDD disease course (Steinert et al., 2014), a statistical model that accounts for underlying individual trajectories over multiple time points may be more suitable. Rather than focusing on overall differences between related means of a given outcome variable across all participants, multilevel models estimate an underlying trajectory across all time points within each participant (Bollen & Curran, 2006). Moreover, multilevel models can provide a more nuanced approach to exploring potential cause-and-effect relationships by accounting for individual differences and time-related effects more effectively (Raudenbush, 2001). The lack of longitudinal imaging data in adult patients with MDD comprising multiple scans per individual over several years represents a crucial gap in the literature. This data in combination with differentiated assessment of the clinical course within follow-up intervals is indispensable to model and understand individual trajectories of brain structure in the long-term course of MDD. So far, little research analyzed gray matter trajectories associated with self-reported symptoms of depression over multiple scan waves in community samples of children and adolescents (Bos et al., 2018; Luby et al., 2016; Luking et al., 2022). One study indicated accelerated cortical thinning in the frontal lobe related to depressive symptoms (Bos et al., 2018) while others report a decline for global GMV and thickness (Luby et al., 2016). Nonetheless, generalization to adult MDD is questionable and the studies vary in their selection of ROIs and assessment of depressive symptoms. To fill this gap, we present the first longitudinal study that investigates individual trajectories of brain structure (GMV and cortical thickness) in the DLPFC, insula and hippocampus in association with duration in MDD including 3 to 7 scans per person covering up to 12 years. Our current investigation uses data from an ongoing multimodal longitudinal study of neurobiology in affective disorders. Participants with and without a diagnosis of MDD are re-assessed every two years undergoing MRI and clinical measurements. Patients with MDD were hospitalized at baseline assessment and recurrence of depressive episodes was determined by trained personnel at all assessments in a clinical interview. Due to common challenges of longitudinal studies, e.g. the correlation between repeated measures on the same person, irregularly timed data and most importantly missing data (Garcia & Marder, 2017), a multilevel modeling approach will be applied. To this end, we will first analyze associations of baseline GMV and cortical thickness of the DLPFC, insula and hippocampus with lifetime duration in MDD (analysis 1a and 1b). Then, duration in MDD per interval will be tested as a time varying predictor for GMV and cortical thickness of the three ROIs in multilevel models (MLM) including minimum three scans per person (analysis 2a and 2b). Finally, we rerun these models and account for psychopharmacological treatment effects by adding medication as an additional predictor (analysis 3a and 3b).

Three important methods areas of multivariate analysis that are not always thought of in terms of latent variable constructs, but for which latent variable modeling can be used to great advantage, are discussed. These methods are: (1) random coefficients describing individual differences in growth; (2) unobserved variables corresponding to missing data; and (3) variance components describing data from cluster sampling. An educational achievement dataset of longitudinal observations on secondary mathematics achievement (the National Longitudinal Study of American Youth) is described as a motivating example. It is shown that all three topics can be simply expressed in terms of latent variable modeling that fits into existing and generally available structural modeling software. This approach makes possible a connection between psychometricians and other methodologists interested in latent variable modeling. Interesting extensions of these statistical analyses are discussed. One table presents missing data patterns.

Using data from the IGEL intervention study, which was conducted in 54 German primary school classes during the 2010/2011 school year, our study aims to reanalyze the effectiveness of the adaptive teaching methods trained in IGEL using a multidimensional multilevel 2pl IRT approach. We assume that such scaling allows for a deeper understanding of how the IGEL interventions foster students' learning.

Dissertation study for Michael Ovalle. Studying "emotional transmission" between parents and children using daily diary data.

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and process quality indicators of education such as student to class ratio, student to teacher ratio, availability of teaching learning resources at school, teaching methods, and standard of course curriculum are found to be significantly affecting the academic achievement of the students. The effects of student level covariates: absence from class, academic motivation, academic self concept, study time, family income, mother's education, parents' employment status, work demand at home, and parent's follow-up of child are significantly varying from school to school. The results show that a large proportion of academic achievement variation is accounted to between schools. It is interesting to found out that the within school variation is very high for government schools while the between school variation is very high for private schools. There is uniformity across the government schools with high individual differences among students. However, there is lesser uniformity across the private schools with lesser individual differences of students. The findings in this study indicate that private schools are in a better position in maintaining quality of education at grade eight. Efficient academic management is needed at the government schools that can improve quality of education at the level.

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and process quality indicators of education such as student to class ratio, student to teacher ratio, availability of teaching learning resources at school, teaching methods, and standard of course curriculum are found to be significantly affecting the academic achievement of the students. The effects of student level covariates: absence from class, academic motivation, academic self concept, study time, family income, mother's education, parents' employment status, work demand at home, and parent's follow-up of child are significantly varying from school to school. The results show that a large proportion of academic achievement variation is accounted to between schools. It is interesting to found out that the within school variation is very high for government schools while the between school variation is very high for private schools. There is uniformity across the government schools with high individual differences among students. However, there is lesser uniformity across the private schools with lesser individual differences of students. The findings in this study indicate that private schools are in a better position in maintaining quality of education at grade eight. Efficient academic management is needed at the government schools that can improve quality of education at the level.

The main purpose of this study is to improve estimation efficiency in obtaining full-information maximum likelihood (FIML) estimates of contextual effects in the framework of a nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM; Cai, 2008, 2010a, 2010b). Results indicate that the MH-RM algorithm can produce FIML estimates and their standard errors efficiently, and the efficiency of MH-RM was more prominent for a cross-level interaction model, which requires five dimensional integration. Simulations, with various sampling and measurement structure conditions, were conducted to obtain information about the performance of nonlinear multilevel latent variable modeling compared to traditional hierarchical linear modeling. Results suggest that nonlinear multilevel latent variable modeling can more properly estimate and detect a contextual effect and a cross-level interaction than the traditional approach. As empirical illustrations, two subsets of data extracted from The Programme for International Student Assessment (PISA, 2000; OECD, 2000) were analyzed. Two appendices are included: (1) Observed and complete data likelihoods; and (2) First and second order derivatives of the complete data models. [The work reported herein received additional support from the Society of Multivariate Experimental Psychology Dissertation Support Awards.]

This research sought to determine whether any measure(s) used in the Carnegie Foundation's classification of Doctoral/Research Universities contribute to a greater degree than other measures to final rank placement. Multilevel Modeling (MLM) was applied to all eight of the Carnegie Foundation's predictor measures using final rank (doctoral/research, high research, very high research) as the outcome (dependent) variable. Data came directly from the Carnegie Foundation. One additional variable, private or public control came from the IPEDS Peer Analysis System. All measures used in the Carnegie Foundation's analyses exhibited strong interrelationships (multicollinearity), which reduces the reliability of multivariate analyses. The overall MLM regression model predicted approximately 50% of the variance in rank, with an estimated multiple r of 0.72. The most powerful predictor of rank was federal science & engineering (S&E) expenditures. Once this variable entered the prediction model, only doctorates granted in the humanities added significantly to prediction (3.5% of variance). Due to the multiplicity inherent to MLM analyses, significance for all tests was set at p less than 0.001. Although both the number of post doctoral appointments (Spearman Rranks = 0.86) and non-faculty researchers (0.67) exhibit strong simple relationship with rank when S&E expenditures and humanities doctorates are entered into the MLM model, the unique contribution of both post doctoral appointments and non-faculty researchers proved to add both non-significant and negative increments in predicting Carnegie rank. Most simple relationships between predictor variables and the outcome Carnegie rank ranged between 0.75 (number of faculty) and 0.89 (S&E expenditures). All of the measures also exhibit strong relationships with other predictors. That the number of faculty has a simple Rranks of 0.75 indicates that a research institution's size alone relates to their rank. Using eight predictor measures, all interrelate strongly and significantly, is effectively like using a single measure to rank institutions. An institution's S&E expenditures may thus be effectively used as that single predictor, although doctorates in the humanities can also influence an institution's rank. Appended is: Reasons for Using Multilevel Modeling Rather than OLS Statistics. (Contains 3 footnotes, 1 figure, and 3 tables.) [This report represents an Internal Technical Report, Office of Planning and Analysis, University of South Florida, Tampa, Florida]

In this paper, to improve power factor a simple model based on five levels multiple pole is used. It is also used to improve harmonic distortion and efficiency which is done by using reduced number of component counts. Most of the work has been done to obtain these factors. In this project, 5L-M2UPFR is used which give almost unity power factor. By this method, the unity power factor with input current shaping is obtained with less number of measurement components. This paper uses balanced and unbalanced load condition over which these improved response is obtained. Ranjan Kumar Rai | Umashankar Patel"An Analysis and Modeling of Grid Connected Multiple-Pole Multilevel Unity Power Factor Rectifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7137.pdf  http://www.ijtsrd.com/engineering/electrical-engineering/7137/an-analysis-and-modeling-of-grid-connected-multiple-pole-multilevel-unity-power-factor-rectifier/ranjan-kumar-rai

In this paper, to improve power factor a simple model based on five levels multiple pole is used. It is also used to improve harmonic distortion and efficiency which is done by using reduced number of component counts. Most of the work has been done to obtain these factors. In this project, 5L-M2UPFR is used which give almost unity power factor. By this method, the unity power factor with input current shaping is obtained with less number of measurement components. This paper uses balanced and unbalanced load condition over which these improved response is obtained. Ranjan Kumar Rai | Umashankar Patel"An Analysis and Modeling of Grid Connected Multiple-Pole Multilevel Unity Power Factor Rectifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7137.pdf Article URL: http://www.ijtsrd.com/engineering/electrical-engineering/7137/an-analysis-and-modeling-of-grid-connected-multiple-pole-multilevel-unity-power-factor-rectifier/ranjan-kumar-rai

This article is from Frontiers in Psychology , volume 5 . Abstract Evaluating model fit in nonlinear multilevel structural equation models (MSEM) presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are non-normally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of non-normality, they have not yet been investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.

This paper analyzed a single phase nine level cascaded h-bridge multilevel inverter (CHMLI) topology in which requires lesser number of components and easier to control if compared to other methods. The inverter of different voltage levels (up to 9-Levels) are designed and simulated using MATLAB Simulink. A Level Shifted Multi-Carrier PWM control scheme is used to control the operation of the power switches for CHMLI topology. The results are compared in terms of THD. The value of THD is found to be reduced as the number of voltage level of CHMLI is increased.

This paper analyzed a single phase nine level cascaded h-bridge multilevel inverter (CHMLI) topology in which requires lesser number of components and easier to control if compared to other methods. The inverter of different voltage levels (up to 9-Levels) are designed and simulated using MATLAB Simulink. A Level Shifted Multi-Carrier PWM control scheme is used to control the operation of the power switches for CHMLI topology. The results are compared in terms of THD. The value of THD is found to be reduced as the number of voltage level of CHMLI is increased.

This paper illustrates the differences in inference that can be seen when traditional and multilevel structural equation modeling techniques are applied to hierarchical data. Research on faculty is an area in which multilevel data exist, and where previous research generally has not modeled the nested structure. Using data from the National Study of Postsecondary Faculty (NSPOF), this paper demonstrates a method for analyzing data that contain measurement error and come from multilevel structures. The NSPOF database contains responses from 25,780 faculty randomly chosen from 817 participating institutions. The integration of multilevel regression modeling and structural equation modeling, which can facilitate proper inference, is used to study faculty satisfaction. In this study, no substantive differences were shown when traditional and more complex modeling techniques were compared, but the results from the analyses contribute to the knowledge base regarding the institutional and job characteristics that can affect faculty satisfaction. Two appendixes contain the NSPOF questionnaire and the EQS syntax for the "independence model." (Contains 2 tables, 7 figures, and 16 references.) (SLD)

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random effect for each institution into the statistical model; moreover, the variability in these random effects is taken into account in estimating the standard errors. Until the advent of HLM, heterogeneity of regression had often been viewed as a methodological nuisance. However, the cause of heterogeneity of regression is often of substantive interest. HLMs enable a researcher to estimate a separate set of regression coefficients for each higher level organizational unit and then model variation among the higher level units in their sets of coefficients as multivariate outcomes to be explained by higher level factors. HLMs solve the problem of aggregation bias by modeling each level of the hierarchy with its own model. Today, many higher education scholars are rushing to use this new, sophisticated analytic procedure. This rush seems to be based on the assumption that HLM might yield substantively different findings than those from studies based on ordinary least squares (OLS) regression analyses. With this in mind, the current study investigates the different conclusions that may be drawn depending upon the type of analysis chosen. This paper focuses on the three types of analyses discussed above. The first analysis will be an OLS regression with the student as the unit of analysis, the second analysis will be an OLS regression with the student level variables aggregated to the institutional level with the institution as the unit of analysis, and the third analysis will be a three-level hierarchical linear model with student characteristics modeled at Level 1, characteristics about the major modeled at Level 2 and characteristics of the institution modeled at Level 3. Appended are: (1) Items comprising the variables used in the analyses and the construction of scales; and (2) List of Majors and Biglan (1973a, 1973b) classification. (Contains 8 tables.)

Childhood is the crucial period for early children's reading ability building. Former research (Hanson & Farrell, 1995) found that early reading experience had a positive and long-term effect on reading competence for high school seniors in the future. Therefore, it is of great importance for researchers to understand children's initial reading abilities, their growth trajectories over time, and furthermore, the effects of child and family characteristics on the growth trajectories. The purpose of the study was to illustrate the use of the multilevel modeling approach to assess the early children's reading growth from their entering the kindergarten through the first year in elementary school. The research questions mainly focused on: (1) how child's reading ability grew over time, (2) how the growth varied across children, and (3) how some child-level variables affected the initial status, and the rate of change of reading ability over time. The data was collected from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K). SAS PROC MIXED procedure was used for analyzing multilevel growth models. The fixed effects and variance components of the fitted models were interpreted. The prototypical growth trajectories of reading ability were plotted. The results indicated that children's reading ability improved considerably during the first two years of schooling. However, there was great variability in individual change over time. An additional finding was that the relationship between the initial status and rate of change in reading ability was positive and small. This study also found two child-level variables, how often parents read to children, and whether children receive pre-kindergarten daycare were significant predictors of the growth trajectories, either initial status or rate of change. Although tentative, our findings do suggest efforts for improving early-reading skills. (Contains 3 figures and 4 tables.)

Multilevel modeling is a statistical approach to analyze hierarchical data, which consist of individual observations nested within clusters. Bayesian methods is a well-known, sometimes better, alternative of Maximum likelihood methods for fitting multilevel models. Lack of user-friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of available software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed. [This paper was published in "Structural Equation Modeling" v25 n4 p650-658 2018.]

This study examined first-year attrition at a large, urban university in the United Kingdom, demonstrating the application of multilevel modeling to the issue of student attrition. A sample of 2,679 full-time, first-year students studying the 20 most common subject areas was identified. Students were divided into four groups depending on their entry route: entering after the 6th year of secondary school after obtaining sufficient Scottish school-leaving qualifications to enter the university in the 5th year; entering with English school-leaving qualifications (which require 6 years of secondary school); entering with Scottish school-leaving qualifications obtained in the year of their leaving secondary school (5th or 6th year); and entering with non-school-leaving qualifications, mainly gained at college. Students' subsequent first-year dropout rates were, respectively, 5.6 percent, 7.6 percent, 11.6 percent, and 18.2 percent. A multilevel random coefficient model was fitted to the data. Data from logistic regression analysis and multilevel analysis indicated that students studying certain subjects had significantly different withdrawal rates. There was little evidence of an interaction effect between subjects and entry routes. There were some significant differences among the withdrawal rates of students in the four entry route groups. (Contains 30 references.) (SM)

This article is from PLoS ONE , volume 9 . Abstract Background: Surgical site infection (SSI) surveillance is a key factor in the elaboration of strategies to reduce SSI occurrence and in providing surgeons with appropriate data feedback (risk indicators, clinical prediction rule). Aim: To improve the predictive performance of an individual-based SSI risk model by considering a multilevel hierarchical structure. Patients and Methods: Data were collected anonymously by the French SSI active surveillance system in 2011. An SSI diagnosis was made by the surgical teams and infection control practitioners following standardized criteria. A random 20% sample comprising 151 hospitals, 502 wards and 62280 patients was used. Three-level (patient, ward, hospital) hierarchical logistic regression models were initially performed. Parameters were estimated using the simulation-based Markov Chain Monte Carlo procedure. Results: A total of 623 SSI were diagnosed (1%). The hospital level was discarded from the analysis as it did not contribute to variability of SSI occurrence (p  = 0.32). Established individual risk factors (patient history, surgical procedure and hospitalization characteristics) were identified. A significant heterogeneity in SSI occurrence between wards was found (median odds ratio [MOR] 3.59, 95% credibility interval [CI] 3.03 to 4.33) after adjusting for patient-level variables. The effects of the follow-up duration varied between wards (p

This study introduces an innovative meta-analytic approach, two-stage multilevel meta-analysis that considers the hierarchical structure of single-case experimental design (SCED) data. This approach is unique as it is suitable to include moderators at the intervention level, participant level, and study level, and is therefore especially recommended for the meta-analyst interested in moving beyond estimating the overall intervention effectiveness. Using this approach, the between-participant variability and between-study variability in intervention effectiveness can be evaluated in addition to obtaining a generalized effect size estimate across studies. This is a timely contribution to the SCED field, as the source(s) of variability in effect size can be identified, and moderators at the corresponding level(s) (participant level and/or study level) can be added to explain the variability. The two-stage multilevel meta-analytic approach, with the inclusion of moderators, can provide evidence-based recommendations about the effectiveness of an intervention taking into account intervention, participant, and study characteristics. First, a conceptual introduction to two-stage multilevel meta-analysis is given to provide a good understanding of its full potentials and modeling options. Second, the usage of this approach will be demonstrated by applying it to a published meta-analytic data set. The goal of this study is to disseminate the two-stage multilevel meta-analysis approach in the hope that SCED meta-analyst will consider this methodology in future meta-analyses.

This article is from ARYA Atherosclerosis , volume 9 . Abstract BACKGROUND: Hypertension is a health problem in Iran. Given the importance of this subject, we reviewed the factors affecting the blood pressure in this survey. METHODS: This retrospective cohort study was performed on 3961 male workers employed at Isfahan Polyacryl Corporation (Iran) in health and safety executive between 1996 until 2008. In this study, systolic and diastolic blood pressure (SBP and DBP) were considered as dependent variables; body mass index (BMI), age, type of job, marital status, shift work and educational level were considered as independent variables. MLwiN programmer version 2.1 was used to analyze the data. RESULTS: BMI, age, shift work, marital status and educational level had statistical significant association with DBP. The result for SBP was similar to DBP except shift work and educational level that had no statistically significant association. CONCLUSION: The results can be considered in the industry to provide practical solutions to reduce blood pressure.

This study used the multilevel modeling method to analyze the research productivity of 1,104 tenured or tenure track faculty from the 1993 National Study of Postsecondary Faculty. The study compared statistical and substantive results of multilevel modeling (or hierarchical linear modeling) to the traditional regression approach. Two dependent variables measured faculty research productivity: (1) publications over 2 years and dollar amount of external research funding and (2) total external grant dollars for the 1992-93 academic year on which the faculty member was a principal or co-principal investigator. The independent variables included human capital, personal tastes, career status, teaching workload, demographics, and academic discipline. Data analysis indicated that using multilevel modeling was very important in studying faculty productivity. There were significant relationships between several independent variables and faculty publications. The results suggest that the group effect of academic field of study should be accounted for when modeling faculty productivity. The results also suggest that faculty work is extremely complex and cannot be explained using single measures for research productivity. (Contains 30 references.) (SM)

The modeling of longitudinal and multilevel data using a latent variable framework is reviewed. Particular emphasis is placed on growth modeling. Examples are discussed where repeated observations are made on students sampled within classrooms and schools. The concept of a latent variable is a convenient way to represent statistical variation not only in conventional psychometric terms with respect to constructs measured with error, but also in the context of models with random coefficients and variance components. These features are explored. The random coefficient feature is shown to be a useful way to study change and growth over time, while the variance component feature is shown to correctly reflect common cluster sampling procedures. Four tables and four figures are included. (Contains 19 references.) (Author/SLD)

This article is from BMC Health Services Research , volume 14 . Abstract Background: Prevention of mother to child HIV transmission (PMTCT) remains a challenge in low and middle-income countries. Determinants of utilization occur – and often interact - at both individual and community levels, but most studies do not address how determinants interact across levels. Multilevel models allow for the importance of both groups and individuals in understanding health outcomes and provide one way to link the traditionally distinct ecological- and individual-level studies. This study examined individual and community level determinants of mother and child receiving PMTCT services in Tigray region, Ethiopia. Methods: A multistage probability sampling method was used for this 2011 cross-sectional study of 220 HIV positive post-partum women attending child immunization services at 50 health facilities in 46 districts. In view of the nested nature of the data, we used multilevel modeling methods and assessed macro level random effects. Results: Seventy nine percent of mothers and 55.7% of their children had received PMTCT services. Multivariate multilevel modeling found that mothers who delivered at a health facility were 18 times (AOR = 18.21; 95% CI 4.37,75.91) and children born at a health facility were 5 times (AOR = 4.77; 95% CI 1.21,18.83) more likely to receive PMTCT services, compared to mothers delivering at home. For every addition of one nurse per 1500 people, the likelihood of getting PMTCT services for a mother increases by 7.22 fold (AOR = 7.22; 95% CI 1.02,51.26), when other individual and community level factors were controlled simultaneously. In addition, district-level variation was low for mothers receiving PMTCT services (0.6% between districts) but higher for children (27.2% variation between districts). Conclusions: This study, using a multilevel modeling approach, was able to identify factors operating at both individual and community levels that affect mothers and children getting PMTCT services. This may allow differentiating and accentuating approaches for different settings in Ethiopia. Increasing health facility delivery and HCT coverage could increase mother-child pairs who are getting PMTCT. Reducing the distance to health facility and increasing the number of nurses and laboratory technicians are also important variables to be considered by the government.

This article is from BMC Health Services Research , volume 14 . Abstract Background: Prevention of mother to child HIV transmission (PMTCT) remains a challenge in low and middle-income countries. Determinants of utilization occur – and often interact - at both individual and community levels, but most studies do not address how determinants interact across levels. Multilevel models allow for the importance of both groups and individuals in understanding health outcomes and provide one way to link the traditionally distinct ecological- and individual-level studies. This study examined individual and community level determinants of mother and child receiving PMTCT services in Tigray region, Ethiopia. Methods: A multistage probability sampling method was used for this 2011 cross-sectional study of 220 HIV positive post-partum women attending child immunization services at 50 health facilities in 46 districts. In view of the nested nature of the data, we used multilevel modeling methods and assessed macro level random effects. Results: Seventy nine percent of mothers and 55.7% of their children had received PMTCT services. Multivariate multilevel modeling found that mothers who delivered at a health facility were 18 times (AOR = 18.21; 95% CI 4.37,75.91) and children born at a health facility were 5 times (AOR = 4.77; 95% CI 1.21,18.83) more likely to receive PMTCT services, compared to mothers delivering at home. For every addition of one nurse per 1500 people, the likelihood of getting PMTCT services for a mother increases by 7.22 fold (AOR = 7.22; 95% CI 1.02,51.26), when other individual and community level factors were controlled simultaneously. In addition, district-level variation was low for mothers receiving PMTCT services (0.6% between districts) but higher for children (27.2% variation between districts). Conclusions: This study, using a multilevel modeling approach, was able to identify factors operating at both individual and community levels that affect mothers and children getting PMTCT services. This may allow differentiating and accentuating approaches for different settings in Ethiopia. Increasing health facility delivery and HCT coverage could increase mother-child pairs who are getting PMTCT. Reducing the distance to health facility and increasing the number of nurses and laboratory technicians are also important variables to be considered by the government.

This article is from BMC Health Services Research , volume 14 . Abstract Background: Prevention of mother to child HIV transmission (PMTCT) remains a challenge in low and middle-income countries. Determinants of utilization occur – and often interact - at both individual and community levels, but most studies do not address how determinants interact across levels. Multilevel models allow for the importance of both groups and individuals in understanding health outcomes and provide one way to link the traditionally distinct ecological- and individual-level studies. This study examined individual and community level determinants of mother and child receiving PMTCT services in Tigray region, Ethiopia. Methods: A multistage probability sampling method was used for this 2011 cross-sectional study of 220 HIV positive post-partum women attending child immunization services at 50 health facilities in 46 districts. In view of the nested nature of the data, we used multilevel modeling methods and assessed macro level random effects. Results: Seventy nine percent of mothers and 55.7% of their children had received PMTCT services. Multivariate multilevel modeling found that mothers who delivered at a health facility were 18 times (AOR = 18.21; 95% CI 4.37,75.91) and children born at a health facility were 5 times (AOR = 4.77; 95% CI 1.21,18.83) more likely to receive PMTCT services, compared to mothers delivering at home. For every addition of one nurse per 1500 people, the likelihood of getting PMTCT services for a mother increases by 7.22 fold (AOR = 7.22; 95% CI 1.02,51.26), when other individual and community level factors were controlled simultaneously. In addition, district-level variation was low for mothers receiving PMTCT services (0.6% between districts) but higher for children (27.2% variation between districts). Conclusions: This study, using a multilevel modeling approach, was able to identify factors operating at both individual and community levels that affect mothers and children getting PMTCT services. This may allow differentiating and accentuating approaches for different settings in Ethiopia. Increasing health facility delivery and HCT coverage could increase mother-child pairs who are getting PMTCT. Reducing the distance to health facility and increasing the number of nurses and laboratory technicians are also important variables to be considered by the government.

Elementary and middle school students who are blind or visually impaired (VI) lag up to three years behind non-disabled peers in mathematics achievement. We investigated the impact of academic supports in the school on mathematics achievement, controlling grade, gender, cognitive disability, and family SES. Data were from SEELS (Special Education Elementary Longitudinal Study) that followed a national sample of students over six years. Analyses employed multilevel modeling. We found the extent of academic supports in the school was positively related to mathematics achievement for visually impaired (VI ) students without cognitive disability but not for those with cognitive disability. Gender and socio-economic status (SES) had no effects. Achievement growth was not hampered by cognitive disability. Schools with more academic supports may enhance mathematics learning for VI students without a cognitive disability, and VI students with a cognitive disability may need both a high level of supports and specialized supports to facilitate mathematics achievement. (Contains 1 figure and 1 table.)

This article is from BMC Health Services Research , volume 14 . Abstract Background: In primary care, patients with multiple chronic conditions are the rule rather than the exception. The Chronic Care Model (CCM) is an evidence-based framework for improving chronic illness care, but little is known about the extent to which it has been implemented in routine primary care. The aim of this study was to describe how multimorbid older patients assess the routine chronic care they receive in primary care practices in Germany, and to explore the extent to which factors at both the practice and patient level determine their views. Methods: This cross-sectional study used baseline data from an observational cohort study involving 158 general practitioners (GP) and 3189 multimorbid patients. Standardized questionnaires were employed to collect data, and the Patient Assessment of Chronic Illness Care (PACIC) questionnaire used to assess the quality of care received. Multilevel hierarchical modeling was used to identify any existing association between the dependent variable, PACIC, and independent variables at the patient level (socio-economic factors, weighted count of chronic conditions, instrumental activities of daily living, health-related quality of life, graded chronic pain, no. of contacts with GP, existence of a disease management program (DMP) disease, self-efficacy, and social support) and the practice level (age and sex of GP, years in current practice, size and type of practice). Results: The overall mean PACIC score was 2.4 (SD 0.8), with the mean subscale scores ranging from 2.0 (SD 1.0, subscale goal setting/tailoring) to 3.5 (SD 0.7, delivery system design). At the patient level, higher PACIC scores were associated with a DMP disease, more frequent GP contacts, higher social support, and higher autonomy of past occupation. At the practice level, solo practices were associated with higher PACIC values than other types of practice. Conclusions: This study shows that from the perspective of multimorbid patients receiving care in German primary care practices, the implementation of structured care and counseling could be improved, particularly by helping patients set specific goals, coordinating care, and arranging follow-up contacts. Studies evaluating chronic care should take into consideration that a patient’s assessment is associated not only with practice-level factors, but also with individual, patient-level factors. Trial registration: Current Controlled Trials ISRCTN89818205.