A bunch of rocks The surface of Titan. So you give someone an inch and they want a yard. Given them a rocket ship and suddenly they want a star ship. SF writers want to use exotic settings on alien planets, but the real estate in our solar system mostly looks like a bunch of rocks.
The Upper Confidence Limit: Multilevel Statistical Models Many kinds of data, including observational data collected in the human and biological sciences, have a hierarchical or clustered structure.
For example, animal and human studies of inheritance deal with a natural hierarchy where offspring are grouped within families. Offspring from the same parents tend to be more alike in their physical and mental characteristics than individuals chosen at random from the population at large.
Many designed experiments also create data hierarchies, for example clinical trials carried out in several randomly chosen centers or groups of individuals.
Multilevel models are concerned only with the fact of such hierarchies not their provenance. We refer to a hierarchy as consisting of units grouped at different levels.
Thus offspring may be the level 1 units in a 2-level structure where the level 2 units are the families: The existence of such data hierarchies is not accidental and should not be ignored. Individual people differ as do individual animals and this necessary differentiation is mirrored in all kinds of social activity where the latter is often a direct result of the former, for example when students with similar motivations or aptitudes are grouped in highly selective schools or colleges.
In other cases, the groupings may arise for reasons less strongly associated with the characteristics of individuals, such as the allocation of young children to elementary schools, or the allocation of patients to different clinics.
Once groupings are established, even if their establishment is effectively random, they will tend to become differentiated, and this differentiation implies that the group' and its members both influence and are influenced by the group membership.
To ignore this relationship risks overlooking the importance of group effects, and may also render invalid many of the traditional statistical analysis techniques used for studying data relationships.
A simple example will show its importance. A well known and influential study of primary elementary school children carried out in the 's claimed that children exposed to so called 'formal' styles of teaching reading exhibited more progress than those who were not. The data were analyzed using traditional multiple regression techniques, which recognized only the individual children as the units of analysis and ignored their groupings within teachers and into classes.
The results were statistically significant.
Subsequently, it has been demonstrated that when the analysis accounted properly for the grouping of children into classes, the significant differences disappeared and the 'formally' taught children could not be shown to differ from the others.
This re-analysis is the first important example of a multilevel analysis of social science data. In essence what was occurring here was that the children within any one classroom, because they were taught together, tended to be similar in their performance.
As a result they provide rather less information than would have been the case if the same number of students had been taught separately by different teachers. In other words, the basic unit for purposes of comparison should have been the teacher not the student.
The function of the students can be seen as providing, for each teacher, an estimate of that teacher's effectiveness. Increasing the number of students per teacher would increase the precision of those estimates but not change the number of teachers being compared.
Beyond a certain point, simply increasing the numbers of students in this way hardly improves things at all. On the other hand, increasing the number of teachers to be compared, with the same or somewhat smaller number of students per teacher, considerably improves the precision of the comparisons.
Researchers have long recognized this issue. In education, for example, there has been much debate about the so called 'unit of analysis' problem, which is the one just outlined.
Before multilevel modelling became well developed as a research tool, the problems of ignoring hierarchical structures were reasonably well understood, but they were difficult to solve because powerful general purpose tools were unavailable.
Special purpose software, for example for the analysis of genetic data, has been available longer but this was restricted to 'variance components' models and was not suitable for handling general linear models.
Sample survey workers have recognized this issue in another form.Erotic literature comprises fictional and factual stories and accounts of human sexual relationships which have the power to or are intended to arouse the reader sexually. Such erotica takes the form of novels, short stories, poetry, true-life memoirs, and sex manuals.A common feature of the genre is sexual fantasies on such themes as prostitution, orgies, homosexuality, sadomasochism, and.
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In a stand-alone literature review, this statement will sum up and evaluate the state of the art in this ﬁeld of research; in a review that is an introduction or preparatory to a thesis or research report, it will suggest how the review ﬁndings will lead to the research the writer proposes to undertake.
A literature survey or a literature review in a project report is that section which shows the various analyses and research made in the field of your interest and the results already published, taking into account the various parameters of the project and the extent of the project.
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