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This study used structural equation modeling and multilevel modeling approaches for purposes of simultaneous study of individual and group change patterns on three waves of two longitudinally assessed domains. This study illustrates a few of the dual approaches to the analysis of covariance structures as they relate to the same individual growth model and the same data. LISREL (structural equation modeling) and hierarchical linear modeling (HLM) were used. The investigation studied whether individual change over time in mathematics and language differs from student to student and whether the individual growth parameters of each of the two domains were related within domain. The study used panel data from the Louisiana State Department of Education for 3 waves of students tested in grades 4, 6, and 7: (1) 50,907; (2) 47,003; and (3) 50,157. Assessments from the states testing program were administered in each of the three grades. Complete records for all 3 grades were available for 26,051 students, 11,627 of whom were African American. The study sheds light on the understanding of learners from the two ethnic groups and shows how they develop mastery in mathematics and language as they progress through school. The application of covariance structure analysis and growth curves to the study of growth in student academic achievement provides an avenue for an in-depth analysis of two academic areas in an available data set. The statistical techniques used in this research, LISREL and HLM methods, have a number of extensions that can be used in various research environments because they can accommodate any number of data points (waves) of longitudinal data. Four appendixes contain descriptive statistics from the study. (Contains 2 figures, 3 tables, and 50 references.) (SLD)

This study used structural equation modeling and multilevel modeling approaches for purposes of simultaneous study of individual and group change patterns on three waves of two longitudinally assessed domains. This study illustrates a few of the dual approaches to the analysis of covariance structures as they relate to the same individual growth model and the same data. LISREL (structural equation modeling) and hierarchical linear modeling (HLM) were used. The investigation studied whether individual change over time in mathematics and language differs from student to student and whether the individual growth parameters of each of the two domains were related within domain. The study used panel data from the Louisiana State Department of Education for 3 waves of students tested in grades 4, 6, and 7: (1) 50,907; (2) 47,003; and (3) 50,157. Assessments from the states testing program were administered in each of the three grades. Complete records for all 3 grades were available for 26,051 students, 11,627 of whom were African American. The study sheds light on the understanding of learners from the two ethnic groups and shows how they develop mastery in mathematics and language as they progress through school. The application of covariance structure analysis and growth curves to the study of growth in student academic achievement provides an avenue for an in-depth analysis of two academic areas in an available data set. The statistical techniques used in this research, LISREL and HLM methods, have a number of extensions that can be used in various research environments because they can accommodate any number of data points (waves) of longitudinal data. Four appendixes contain descriptive statistics from the study. (Contains 2 figures, 3 tables, and 50 references.) (SLD)

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

1Little Sister Snow

By Frances Little

American author Fannie Caldwell, under pen name of Frances Little, tells the story of young Yuki San growing up in Japan circa early 1900s, and of her dreams of an American. (Introduction by Cheri Gardner)

2Little Sister Snow (version 2)

By Frances Little

This is a story of a little Japanese girl, her life in Japan and her loves. The story opens just before the festival of Hinamatsuri on the third day of the third month, which was set apart as the big birthday of all little girls born in the lovely island, and was celebrated by the Festival of Dolls, which is celebrated on March 3rd throughout Japan for the well being of young girls, praying for their prosperous health. Isn’t it touching? Here is this country (Japan) who graciously honors a girl child through an ancient festival for their safety expunging the bad spirits from the dolls. <br> Yuki San is the young daughter of an old Japanese couple. She's spoiled, sassy, and (in my opinion) quite naughty. The couple tried for many, many years to have a baby and finally Yuki was born. In their eyes she can do no wrong. She is their blessing and will care for them in their final years of life. One day Yuki decides to drown her kitten by throwing it into a gutter that leads to the ocean. Her plan is interrupted by an American teen, Richard Merrit but that's all I'm going to say. You will need to listen to this wonderful tale to find out the surprising stuff that happens to them both. Oh, there is a pre-arranged marriage involved here. (Summary by Phil Chenevert)

Maybe--tomorrow, by Jay Little (pseudonym for Clarence Lewis Miller) published in 1952* based in the confusing latter part of his teenage years, tells the story of the introverted and forlorn Gaylord LeClarie coming to terms with the world around him and who he is. Gaylord must navigate everything from sex, his own sexuality and his own gender identity. friendship, Love and self-acceptance in a sometimes hostile world... - Summary by Curt Troutwine