### 统计代写|SPSS代写代考|Generalizability and sampling adequacy

SPSS主要用于数据管理、高级分析、多变量分析、商业智能。

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|SPSS代写代考|Generalizability and sampling adequacy

As we have alluded to so far in this chapter, one of the reasons that sampling, and sampling bias, are important is about generalizability. Usually, when a researcher conducts a quantitative study, they hope to have results that mean something for the population. In other words, researchers usually study samples to find things out about the population. When samples are too biased or too unrepresentative, the results may not generalize at all. That is, in a very biased sample, the results might only apply to that sample and be unlikely to ever occur in any other group. Generalizability, then, is often a goal of

quantitative work. Very few samples’ results would generalize to the entire population, but researchers should think about how far their results might generalize. One way to assess the generalizability of results is to evaluate sampling biases.

Another issue in generalizability is related to sample size. How many people comprise a sample affects multiple layers of quantitative analysis, including factors we will come to in future chapters like normality and homogeneity of variance. But the sample size also impacts generalizability. Very small samples are much less likely to be representative of the population. Even by pure chance in a random sample, smaller samples are more likely to be biased. As the sample size increases, it will likely become more representative. In fact, as the sample size increases, it gets closer and closer to the size of the population. As a general rule, there are some minimum sample sizes in quantitative research. We’ll return to these norms in future chapters. Most of our examples in this text will involve very small, imaginary samples to make it easier to track how the analyses work. But in general samples should have at least 30 people for a correlational or within-subjects design. When comparing two or more groups, the minimum should be at least 30 people per group (Gay et al., 2016). These are considered to be minimum sample sizes, and much larger samples might be appropriate in many cases, especially where there are multiple variables under analysis or the differences are likely to be small (Borg \& Gall, 1979).

## 统计代写|SPSS代写代考|LEVELS OF MEASUREMENT

The data we gather can be measured at several different levels. In the most basic sense, we think of variables as being either categorical or continuous. Categorical variables place people into groups, which might be groups with no meaningful order or groups that have a rank order to them. Continuous variables measure a quantity or amount, rather than a category. There are two types of categorical variables: nominal and ordinal. Likewise, there are two types of continuous variables: interval and ratio. For the purposes of the analyses discussed in this book, differentiating between interval and ratio data will not be important. However, below we introduce each level of measurement and provide some examples.

## 统计代写|SPSS代写代考|Nominal

Nominal data involve named categories. Nominal data cannot be meaningfully ordered. That is, they are categorical data with no meaningful numeric or rank-ordered values. For example, we might categorize participants based on things like gender, city of residence, race, or academic program. These categories do not have meaningful ordering or numbering within them-they are simply ways of categorizing participants. It is also important to note that all of these categories are also relatively arbitrary and rely on social constructions. Nominal data will often be coded numerically, even though the numbers assigned to each group are also arbitrary. For example, in collecting student gender, we might set 1 = woman, 2 = man, 3 = nonbinary/genderqueer, $4=$ an option not included in this list. There is no real logic to which group we assign the label of $1,2,3$, or 4 . In fact, it would make no difference if instead we labelled these groups $24,85,129$, and 72 . The numeric label simply marks which groups someone is in – it has no actual mathematical or ranking value. However, we will usually code groups numerically because software programs, such as jamovi, cannot analyze text data easily. So, we code group membership with numeric codes to make it easier to analyze later on. In another example, researchers in the United States often use racial categories that align to the federal Census categories. They do so in order to be able to compare their samples to the population for some region or even the entire country. So, they might code race as 1 = Black/African American, 2 = Asian American/Pacific Islander, 3 = Native American/Alaskan Native, $4=$ Hispanic/ Latinx, 5 = White. Again, the numbering of these categories is completely arbitrary and carries no real meaning. They could be numbered in any order and accomplish the same goal. Also notice that, although these racial categories are widely used, they are also problematic and leave many racial and ethnic groups out altogether. For most of the analyses covered in this text, nominal variables will be used to group participants in order to compare group means. Another example of a nominal variable would be experimental groups, where we might have $1=$ experimental condition and $0=$ control condition.

## 广义线性模型代考

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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。