统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

statistics-lab™ 为您的留学生涯保驾护航 在代写Generalized linear model方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写Generalized linear model代写方面经验极为丰富，各种代写Generalized linear model相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

统计代写|Generalized linear model代考广义线性模型代写|Defining What to Measure

The first step in measuring quantitative variables is to create an operationalization for them. In Chapter 1, an operationalization was defined as a description of a variable that permits a researcher to collect quantitative data on that variable. For example, an operationalization of “affection” may be the percentage of time that a couple holds hands while sitting next to each other. Strictly speaking, “time holding hands” is not the same as “affection.” But “time holding hands” can be objectively observed and measured; two people measuring “time holding hands” for the same couple would likely produce similar data. However, “affection” is abstract, ambiguous, and unlikely to produce consistent results. Likewise, a researcher could define “attentiveness” as the number of times a parent makes eye contact with a child or the number of times he or she says the child’s name. Again, these operationalizations are not the same thing as “attentiveness,” but they are less ambiguous. More important, they produce numbers that we can then use in statistical analysis.

You may have a problem with operationalizations because using operationalizations means that researchers are not really studying what interests them, such as “affection” or “attentiveness.” Rather, they are studying the operationalizations, which are shallow approximations for the ideas that really interest them. It is understandable why operationalizations are dissatisfying to some: no student declares a major in the social sciences by saying, “lam so excited to learn about the percentage of time that parents hold handsl”
Critics of operationalizations say that – as a result – quantitative research is reductionist, meaning that it reduces phenomena to a shadow of themselves, and that researchers are not really studying the phenomena that interest them. These critics have a point. In a literal sense, one could say that no social scientist has ever studied “affection,” “racism,” “personality, ” “marital satisfaction, ” or many other important phenomena. This shortcoming is not unique to the social sciences. Students and researchers in the physical and biological sciences operationalize concepts like “gravity, ” “animal packs, “and “physical health” in order to find quantifiable ways of measuring them.

There are two responses to these criticisms. First, it is important to keep in mind that quantitative research is all about creating models, which are simplified versions of reality – not reality itself (see Chapter 1). Part of that simplification is creating an operationalization that makes model building possible in the first place. As long as we remember the difference between the model and reality, the simplified, shallow version of reality is not concerning.

My second response is pragmatic (i.e., practical) in nature: operationalizations (and, in turn, models) are simply necessary for quantitative research to happen. In other words, quantitative research “gets the job done,” and operationalizations and models are just necessary parts of the quantitative research process. Scientists in many fields break down the phenomena they study into manageable, measurable parts – which requires operationalization. A philosopher of science may not think that the “because it works” response is satisfactory, but in the day-to-day world of scientific research, it is good enough.

If you are still dissatisfied with my two responses and you see reductionism as an unacceptable aspect of quantitative science, then you should check out qualitative methods, which are much less reductionist than quantitative methods because they focus on the experiences of subjects and the meaning of social science phenomena. Indeed, some social scientists combine qualitative and quantitative research methods in the same study, a methodology called “mixed methods” (see Dellinger \& Leech, 2007). For a deeper discussion of reductionism and other philosophical issues that underlie the social sciences, I suggest the excellent book by Slife and Williams (1995).

统计代写|Generalized linear model代考广义线性模型代写|Levels of Data

Operationalizations are essential for quantitative research, but it is necessary to understand the characteristics of the numerical data that operationalizations produce. To organize these data, most social scientists use a system of organizing data created by Stevens (1946). As a psychophysicist, Stevens was uniquely qualified to create a system that organizes the different types of numerical data

that scientists gather. This is because a psychophysicist studies the way people perceive physical stimuli, such as light, sound, and pressure. In his work Stevens often was in contact with people who worked in the physical sciences – where measurement and data collection are uncomplicated – and the social sciences – where data collection and operationalizations are often confusing and haphazard (Miller, 1975). Stevens and other psychophysicists had noticed that the differences in perceptions that people had of physical stimuli often did not match the physical differences in those stimuli as measured through objective instruments. For example, Stevens (1936) knew that when a person had to judge how much louder one sound was than another, their subjective reports often did not agree with the actual differences in the volume of the two sounds, as measured physically in decibels. As a result, some researchers in the physical sciences claimed that the data that psychologists gathered about their subjects’ perceptions or experiences were invalid. Stevens had difficulty accepting this position because psychologists had a record of success in collecting useful data, especially in studies of sensation, memory, and intelligence.

The argument between researchers in the physical sciences and the social sciences was at an impasse for years. Stevens’s breakthrough insight was in realizing that psychologists and physicists were both collecting data, but that these were different levels of data (also called levels of measurement). In Stevens’s (1946) system, measurement – or data collection – is merely “the assignment of numerals to objects or events according to rules” (p. 677$)$. Stevens also realized that using different “rules” of measurement resulted in different types (or levels) of data. He explained that there were four levels of data, which, when arranged from simplest to most complex, are nominal, ordinal, interval, and ratio data (Stevens, 1946). We will explore definitions and examples of each of these levels of data.

Nominal Data. To create nominal data, it is necessary to classify objects into categories that are mutually exclusive and exhaustive. “Mutually exclusive” means that the categories do not overlap and that each object being measured can belong to only one category. “Exhaustive” means that every object belongs to a category – and there are no leftover objects. Once mutually exclusive and exhaustive categories are created, the researcher assigns a number to each category. Every object in the category receives the same number.

There is no minimum number of the objects that a category must have for nominal data, although it is needlessly complicated to create categories that don’t have any objects in them. On the other hand, sometimes to avoid having a large number of categories containing only one or two objects, some researchers create an “other” or “miscellaneous” category and assign a number to it. This is acceptable as long as the “miscellaneous” category does not overlap with any other category, and all categories together are exhaustive.

统计代写|Generalized linear model代考广义线性模型代写|Other Ways to Classify Data

The Stevens (1946) system is – by far – the most common way to organize quantitative data, but it is not the only possible scheme. Some social scientists also attempt to ascertain whether their data are continuous or discrete. Continuous data are data that permit a wide range of scores that form a constant scale with no gaps at any point along the scale and also have many possible values. Many types of data in the social sciences are continuous, such as intelligence test scores, which in a normal human population range from about 55 to 145 on most tests, with every whole number in between being a possible value for a person.

Continuous data often permit scores that are expressed as fractions or decimals. All three temperature scales that I have discussed in this chapter (i.e., Fahrenheit, Celsius, and Kelvin) are continuous data, and with a sensitive enough thermometer it would be easy to gather temperature data measured at the half-degree or tenth-degree.

The opposite of continuous data are discrete data, which are scores that have a limited range of possible values and do not form a constant, uninterrupted scale of scores. All nominal data are discrete, as are ordinal data that have a limited number of categories or large gaps between groups. A movie rating system where a critic gives every film a $1-, 2-, 3-$, or 4 -star rating would be discrete data because it only has four possible values. Most interval or ratio data, however, are continuous – not discrete – data. The point at which a variable has “too many” values to be discrete and is therefore continuous is often not entirely clear, and whether a particular variable consists of discrete or continuous data is sometimes a subjective judgment. To continue with the movie rating system example, the website Internet Movie Database (IMDb) asks users to rate films on a scale from 1 to 10 . Whether ten categories are enough for the data to be continuous is a matter of argument, and opinions may vary from researcher to researcher.

统计代写|Generalized linear model代考广义线性模型代写|Other Ways to Classify Data

Stevens (1946) 系统是迄今为止最常见的定量数据组织方式，但它并不是唯一可能的方案。一些社会科学家还试图确定他们的数据是连续的还是离散的。连续数据是允许范围广泛的分数的数据，这些分数形成一个恒定的尺度，在尺度上的任何一点都没有间隙，并且还具有许多可能的值。社会科学中的许多类型的数据是连续的，例如智力测试分数，在大多数测试中，在正常人群中的范围从大约 55 到 145，其中每个整数都是一个人的可能值。

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。