The need to ensure that European social scientists are working at the cutting-edge of quantitativemethods is becoming of increasing importance. In many coun- tries there is concern over a shortage of younger social scientists with the necessary training and skills in quan- titative methods. The network will provide a focal point for methodological innovation and advancement and ensure that we develop a new generation of European researchers able to use advanced quantitativemethods across the social sciences. The programme represents a continuation of the very successful work carried out under QuantitativeMethods in the Social Sciences, led by Chris Skinner at the University of Southampton.
Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are human neuromuscular disorders associated with mutations of simple repetitive sequences in affected genes. The abnormal expansion of CTG repeats in the 3′-UTR of the DMPK gene elicits DM1, whereas elongated CCTG repeats in intron 1 of ZNF9/CNBP triggers DM2. Pathogenesis of both disorders is manifested by nuclear retention of expanded repeat- containing RNAs and aberrant alternative splicing. The precise determination of absolute numbers of mutant RNA molecules is important for a better understanding of disease complexity and for accurate evaluation of the efficacy of therapeutic drugs. We present two quantitativemethods, Multiplex Ligation-Dependent Probe Amplification and droplet digital PCR, for studying the mutant DMPK transcript (DMPK exp RNA) and the aberrant alternative splicing in DM1 and DM2 human tissues and
This programme does not set out to address such possible root causes. Rather, it starts from a recognition that, despite such influences and trends, there remain many centres of excellence in quantitative social science in Europe. The programme aims to build upon this position, drawing upon the support of senior researchers to foster the development of a new generation of quantitative researchers, especially by encouraging pan-European networking. The aim is to strengthen links not only across countries but also between social scientists using quantitativemethods in substantive research and social statisticians and others with more methodological interests.
statistical after basic views. He mentioned sufficient advantages and disadvantages in writing a method for quantitative research, considering advantage point of this view we should avoid several problems to compare with each other in the study. He demanded students that accuracy and precision of measuring in research is necessary as variables are observable and quantifiable in the study it can be manifested by other analytical writers, the researcher should place null hypothesis as well as the importance of examining the descriptive statistics, the researcher should avoid to compare multiple problems that multiple dependent variables would be quantified by ANOVA or MANOVA that is possible to analyze the data with more variables, increasing statistical power, broadening research perspective, aligning research analysis more closely to the way that people think, reducing redundancy of variables, expanding types of variables, getting more flexibility in analysis, and simultaneously addressing multiple levels of analysis. It is clearly mentioned profitable points by James Dean Brown to consider advantages of learning statistical analysis and writing a good methodology for the study. He has mentioned disadvantages points as well as advantages, such as we have large sample size produce meaningful interpretation but we do not have this chance in the study to test on large sample, additional assumptions that ANOVA robust to violate additional assumption, need for data screening and complexity of analysis, all explanations are reliable and mainly focus on exact information that a researcher needs to know and it is potentially understood by researchers who have done such studies before or they want to become more powerful in their next steps for future studies. PART II. ENHANCING EXISTING QUANTITATIVEMETHODS
Management students come from different backgrounds, so we cannot assume much common knowledge or interests. In this book we start with the assumption that you have no previous knowledge of management or quantitativemethods. Then the book works from basic principles and develops ideas in a logical sequence, moving from underlying concepts through to real applications. One common observation is that management students can find quantitative ideas difficult or intimidating. You are probably not interested in mathematical abstraction, proofs and derivations – but more in results that you can actually use in business. This is why the book has a practical rather than a theoretical approach. We have made a deliberate decision to avoid proofs, derivations and rigorous (often tedious) mathematics. Some formal procedures are included, but these are kept to a minimum. At the same time we emphasise principles, but leave computers to do the routine calculations. In practice, spreadsheets are a particularly useful tool and we illustrate many ideas with Microsoft Excel (but you can get equivalent results from any spreadsheet).
The last decade has seen a considerable increase in the application of quantitativemethods in the study of histo- logical sections of brain tissue and especially in the study of neurodegenerative disease. These disorders are charac- terised by the deposition and aggregation of abnormal or misfolded proteins in the form of extracellular protein deposits such as senile plaques (SP) and intracellular inclusions such as neurofibrillary tangles (NFT). Quantification of brain lesions and studying the relationships between lesions and normal anatomical features of the brain, includ- ing neurons, glial cells, and blood vessels, has become an important method of elucidating disease pathogenesis. This review describes methods for quantifying the abundance of a histological feature such as density, frequency, and ‘load’ and the sampling methods by which quantitative measures can be obtained including plot/quadrat sampling, transect sampling, and the point-quarter method. In addition, methods for determining the spatial pattern of a his- tological feature, i.e., whether the feature is distributed at random, regularly, or is aggregated into clusters, are described. These methods include the use of the Poisson and binomial distributions, pattern analysis by regression, Fourier analysis, and methods based on mapped point patterns. Finally, the statistical methods available for study- ing the degree of spatial correlation between pathological lesions and neurons, glial cells, and blood vessels are described.
mathematical models to explain the behaviour of security prices and rates of return. It is therefore essential that you acquire a sound knowledge and understanding of the most commonly used mathematical and statistical methods, both in order to be able to read the recent literature on finance and in order to develop further your professional ability in financial management. This course starts by illustrating in Unit 1 the main types of financial securi- ties: bonds and stocks (or shares). After defining each type of security, you will see how we can decide among alternative investment strategies on the basis of the expected returns that each one of them offers. The material covered in this unit is the basis of all financial analysis, and it is crucial that you make yourself perfectly familiar with all the concepts and methods of this unit. The following two units introduce the main statistical ideas in quantitativemethods.
elite players. The difference between elite and sub-elite players may be due to the methods of power training used by sub-elite players and their age level. The significant correlation between bench press and back squat performance in the current study may be explained by the intensity of strength training for the upper and lower body that takes place in rugby. Rugby demands physical contact with the upper body and power training for the lower body so that players will be strong and able to push their opponents during contacts such as scrimmaging. Few previous studies of rugby have investigated this point. Therefore, further investigation of this topic is necessary. It is recommended that strength coaches include this type of exercise and percentages in (1RM) during resistance weight training, in which the number of repetitions is an important variable.
DOI: 10.4236/jamp.2017.59155 1838 Journal of Applied Mathematics and Physics In the case of Kovalevskaya, the problem reduced to quadrature and the integral was obtained through a Remain θ -function of two variables . These functions however are not single-valued, and on the other hand, the functions do not have branch point and hence they are root functions . Consequently, the qualitative and quantitative investigations give us more understanding of the motion of the problem.
Measurement is important. Recognizing that fact, and respecting it, will be of great benefit to you—both in research methods and in other areas of life as well. If, for example, you have ever baked a cake, you know well the importance of measurement. As someone who much prefers rebelling against precise rules over following them, I once learned the hard way that measurement matters. A couple of years ago I attempted to bake my husband a birthday cake without the help of any measuring utensils. I’d baked before, I reasoned, and I had a pretty good sense of the difference between a cup and a tablespoon. How hard could it be? As it turns out, it’s not easy guesstimating precise measures. That cake was the lumpiest, most lopsided cake I’ve ever seen. And it tasted kind of like Play-Doh. depicts the monstrosity I created, all because I did not respect the value of measurement. Just as measurement is critical to successful baking, it is as important to successfully pulling off a social scientific research project. In sociology, when we use the term measurement we mean the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating. At its core, measurement is about defining one’s terms in as clear and precise a way as possible. Of course, measurement in social science isn’t quite as simple as using some predetermined or universally agreed-on tool, such as a
This assumption needs to be thoroughly considered by health services managers who use analytic forecasting approaches. For example, if the assignment is to fore- cast the number of patient days a hospital will generate (or produce) in the follow- ing month, basing the forecast on past patient-day production or generation seems appropriate because the past may be a reasonable predictor of the future. Examine the data in Figure 5-1. By a quick visual scan, if asked to predict the next month’s visits, most would place the forecast near 100. This, however, would be a visual judgment forecast. Instead there are methods to mathematically use past data to predict the future within some range of certainty. One question that first must be answered is, How far back should past data be included in making the forecast? Another is, How far into the future can and should one forecast given the data at hand? What if, for example, you were asked to forecast hospital patient days for the next 10 years? Ten years is a very long time in the future, and many unknown variables could affect the accuracy of such a distanced forecast. Given the ambigu- ity associated with such a long time interval, most health services managers would be very reluctant to base a 10-year forecast solely upon past data.
Road infrastructure has traditionally been regarded as a critical input for the rural economy in general and for the agricultural sector in particular, since it serves to lower transportation costs, to relax constraints to market access, and consequently to boost productivity. Farmers in road-deprived areas cannot easily exploit available market opportunities and so are more likely to choose to produce less risky staple crops over market crops, and to do so using traditional, lower-yield methods. They also tend to under-invest in health and education for themselves and for their children which, in a dynamic setting, lowers their long-term prospects of escaping poverty and food insecurity. One objective of rural road rehabilitation programmes, therefore, is to reverse (or moderate) these adverse outcomes by promoting a higher degree of market integration, the adoption of modern agricultural techniques, and the formation of enhanced human capital.
Teacher review is important method of quality control in education. Teachers are periodically observed by quality control experts, colleagues, or school management in order to assess their success at meeting quality standards. In determining a teacher's performance, quality control officers may interview students, examine recent grades given, and judge whether the methods used in the classroom are truly contribute to education. No doubts, using many different tactics to determine teacher performance level is often considered very important. Among the tactics one of the most objective and reliable is quantitative evaluation of all kind of tests results during the semester.
So far, FoodNet surveillance data has essentially been the object of descriptive analyses. For instance, the annual reports describe temporal trends by contrasting yearly rates and interpreting the potential effect of demographic covariates through frequency tables (CDC, 2000a; CDC, 2000b; CDC, 2000c; CDC, 2000d). The availability of an analytical approach that was able to better harness the multivariate and longitudinal nature of the FoodNet data would provide a more formal and powerful insight. Specific challenges are likely to emerge, though. Firstly, surveillance data are less specific or precise than are those from research studies (Buehler, 1998), and may not be amenable to the assumptions constraining statistical analyses. Such a quantitative approach would also have to respect two specific constraints. The first is the discrete count characteristic of the dependent variable (i.e. number of cases of foodborne infections). Secondly, the likely correlation among repeated measurements needs to be handled. Further, exposure may not be well characterized by the available explanatory variables (place, time, covariate), and the interrelationship among these effects may be complex.
At first glance Research Methods may look like a technical course alongside the more abstract sociological subjects you have encountered thus far. In some sense, this is true –being able to do research is indeed a practical skill! However, an introduction to research methods must encompass more than practical know-how. This is because the domain of social research speaks to key issues around the production of truth. In fact methodology speaks to the heart of academic life, looking at the systems which scholars have come up with over time to gather information about people and social organisation. As you will come to see –methodology is itself a very complex field with many abstract questions which arise from the diverse ways in which new knowledge is produced. Research is an integral part of what C. Wright Mills called our “intellectual craftsmanship”. It is in this spirit that social research methods will be introduced!
Quantitativemethods which examine micro-crystals are valuable because of the many and varied applications of crystals on this scale, as highlighted in Section 1.4.2. Furthermore, as we show herein, micro-crystals are easier to fully characterise (i.e. to determine the reactivity of individual exposed crystal planes). Moreover, if iso- lated, micro-crystals are subject to a well-defined mass transport (di↵usion) regime, as exemplified by electrochemical studies of UMEs . Put simply, just as re- ducing the size of a voltammetric/amperometric UME enhances the di↵usion rate (magnitude proportional to the inverse of the characteristic electrode dimension), so does shrinking the size of an isolated crystal. Thus, as shown in this chapter, one can promote well-defined (and high) di↵usion rates by studying micro-scale crystals. This enhances the opportunity to observe the influence of surface kinetics in hetero- geneous physicochemical processes. Herein, we visualise the growth and dissolution of micro-crystals in situ and use the experimental data obtained as parameters for a FEM model that then reveals the kinetic regime. The importance of di↵usion compared to surface reactions in determining the reactivity is revealed, and the ap- proach allows concentration distributions around growing and dissolving crystals to be predicted.