### 统计代写|SPSS代写代考|Sampling and basic issues in research design

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
• (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|Random sampling

In an ideal situation, researchers would use random sampling. In random sampling, every member of the population has an equal probability of being in the study sample. Imagine we can get a list of all first-year college students in the United States, complete with contact information. We could randomly draw 250 names from that list and ask them to participate in our study. We are likely to find that some of those students will not respond to our invitation. Others might decline to participate. Still others might start the survey but drop out partway through it. Therefore, even with a perfect sampling strategy designed to produce a random sample, we still face a number of barriers that make a true random sample almost impossible. Of course, the other problem with this scenario is that it is nearly impossible to get a list of everyone in the population. Random sampling is impractical in research with human participants, but there are several other strategies that researchers commonly use.

## 统计代写|SPSS代写代考|Representative (quota) sampling

Quota sampling method selects samples based on exact numbers or quotas of individuals or groups with varying characteristics (Gay, Mills, \& Airasian, 2016). We also call this sampling method representative sampling. Many U.S. federal datasets are collected using quota or representative sampling. In this sampling strategy, researchers set targets, or quotas, for people with specific characteristics. Often, those characteristics are demographic variables. For example, some federal education datasets in the United States use the census to determine sampling quotas for the combination of race, sex as assigned at birth, and location. These quotas are usually set to be representative in each demographic category. For example, the U.S. Census showed that Black women comprised $12.85 \%$ of the population of Alabama (U.S. Census Bureau, 2017). A researcher with a goal of 1,000 participants from Alabama might set a quota of 129 Black women for that sample (taking the population percentage and multiplying by the target sample size). The researcher would then intentionally seek out Black women until 129 were enrolled in the study. The researcher would set quotas for every demographic category and then engage in targeted recruiting of that group until the quota was met. The end result is a sample that matches the population very closely in demographic categories. However, the process of producing that representative sample involved many targeted recruiting efforts, which might introduce sampling bias. However, this method is widely used to produce samples that approach representativeness, especially in large-scale and large-budget survey research.

## 统计代写|SPSS代写代考|Snowball sampling

Another method of accessing population that is not easily accessible or hard to reach is snowball sampling. Examples might include members of a secretive group, people with a stigmatized health issue, or members of a group subject to legal restrictions or targeted by law enforcement. A snowball sample begins with the researcher identifying a small number of participants to directly recruit. That initial recruiting might involve relationship and trust building work as well. For example, if a researcher was interested in surveying undocumented immigrants, they might find this population difficult to directly reach because of legal and social factors. So, the researcher might need to invest in building relationships with a small number or local group of undocumented immigrants. In that example, it would be important for the researcher to build some genuine, authentic relationships and to prove that they are trustworthy. Participants might be skeptical of a researcher in this circumstance, wondering about how disclosing their documentation status to a researcher might impact their legal or social situation. It would be important for the researcher to prove they are a safe person to talk to. After initial recruiting in a snowball sample, participants are asked to recruit other individuals that qualify for the study. This is useful because, in some circumstances, individuals who are in a particular social or demographic group might be more likely to know of other people in that same group. It can also be useful because, if the researcher has done the work of building relationships and trust, participants may be comfortable vouching for the researcher with other potential participants. This approach is used in quantitative and qualitative research. One drawback to snowball sampling is it tends to produce very homogenous samples. Because the recruiting or sampling effort is happening entirely through social contacts, the participants who enroll in the study tend to be very similar in sociodemographic factors. In some cases, that similarity is acceptable, but this only works when the criteria for inclusion in the study are relatively narrow.

## 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。