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An instrumental or instrumental song is music normally without any vocals, although it might include some inarticulate vocals, such as shouted backup vocals in a big band setting. Through semantic widening, a broader sense of the word song may refer to instrumentals.[1][2][3] The music is primarily or exclusively produced using musical instruments. An instrumental can exist in music notation, after it is written by a composer; in the mind of the composer (especially in cases where the composer themselves will perform the piece, as in the case of a blues solo guitarist or a folk music fiddle player); as a piece that is performed live by a single instrumentalist or a musical ensemble, which could range in components from a duo or trio to a large big band, concert band or orchestra.
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In a song that is otherwise sung, a section that is not sung but which is played by instruments can be called an instrumental interlude, or, if it occurs at the beginning of the song, before the singer starts to sing, an instrumental introduction. If the instrumental section highlights the skill, musicality, and often the virtuosity of a particular performer (or group of performers), the section may be called a "solo" (e.g., the guitar solo that is a key section of heavy metal music and hard rock songs). If the instruments are percussion instruments, the interlude can be called a percussion interlude or "percussion break". These interludes are a form of break in the song.
In commercial popular music, instrumental tracks are sometimes renderings, remixes of a corresponding release that features vocals, but they may also be compositions originally conceived without vocals. One example of a genre in which both vocal/instrumental and solely instrumental songs are produced is blues. A blues band often uses mostly songs that have lyrics that are sung, but during the band's show, they may also perform instrumental songs which only include electric guitar, harmonica, upright bass/electric bass and drum kit.
The division offers Bachelor of Music, Master of Music, and Doctor of Musical Arts performance degrees with specialization in: violin, viola, violoncello, double bass, harp, guitar, flute, oboe, clarinet, bassoon, saxophone, trumpet, French horn, trombone, euphonium, tuba, percussion, and multiple woodwinds. Current and former students have won prizes in major instrumental competitions of every genre, and are appointed to professional positions in orchestras, wind symphonies, and universities/conservatories spanning the world.
Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.
Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual-level data on the IVs are aggregated into a univariate score, which is used as a single IV, and (ii) a summary statistic method, in which causal estimates calculated from each IV using summarized data are combined in an inverse-variance weighted meta-analysis. To avoid bias from weak instruments, unweighted and externally weighted allele scores have been recommended. Here, we propose equivalent approaches using summarized data and also provide extensions of the methods for use with correlated IVs. We investigate the impact of different choices of weights on the bias and precision of estimates in simulation studies. We show that allele score estimates can be reproduced using summarized data on genetic associations with the risk factor and the outcome. Estimates from the summary statistic method using external weights are biased towards the null when the weights are imprecisely estimated; in contrast, allele score estimates are unbiased. With equal or external weights, both methods provide appropriate tests of the null hypothesis of no causal effect even with large numbers of potentially weak instruments. We illustrate these methods using summarized data on the causal effect of low-density lipoprotein cholesterol on coronary heart disease risk. It is shown that a more precise causal estimate can be obtained using multiple genetic variants from a single gene region, even if the variants are correlated.
The purpose of the Bachelor of Music in Instrumental Education is to prepare the student for a professional career in teaching instrumental music in a group setting. Students are trained to teach individual instruments, as well as instrumental ensembles, which results in public school certification at the K-12 levels.
Graduates of the program have 100% pass rate on the Oklahoma Teacher Certification Exams and 100% placement rate for those who were actively seeking teaching positions. Those not seeking positions have been accepted into graduate programs. Students have accepted positions throughout the U.S., Europe, and South Korea. Several instrumental alumni have been named school and district Teachers of the Year. Two of those alumni have gone on to be named Oklahoma Teacher of the Year (2006, 2013).
I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrumental variables methods and in what settings these may be plausible. By providing context to the current applications, a better understanding of the applicability of these methods may arise.
Since the seminal work of Gardner and Lambert in 1972, language teachers and researchers have recognized the important role that motivation plays in language learning. Gardner and Lambert are responsible for proposing the most commonly used framework for understanding the different motivations that language learners typically have. They distinguish two types of language learning motivation: instrumental motivation and integrative motivation.
Learners with an instrumental motivation want to learn a language because of a practical reason such as getting a salary bonus or getting into college. Many college language learners have a clear instrumental motivation for language learning: They want to fulfill a college language requirement! Integratively motivated learners want to learn the language so that they can better understand and get to know the people who speak that language. In the North American context, integrative motivation has proven to be a strong impetus to successful language learning.
The new language teachers in this video clip discuss their own and their students' instrumental motivations for language learning. The motivations described here range from using the language to study philosophy to imagining a career in beer production. In addition to having different reasons for language learning, some of the learners described here are more strongly motivated than others.
To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.
For more information about Instrumental Neutron Activation Analysis (INAA) follow the links below. University of Massachusetts-Lowell INAA Laboratory - gives an overview of instrumental neutron activation analysis, applications, and links to other useful sitesCanberra.com - technical descriptions of the various components of a gamma ray spectroscopy system.University of Missouri Overview of Neutron Activation Analysis - overview of neutron activation analysis.Wikipedia Neutron Activation Analysis - description of INAA; includes links to other sites.
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