统计代写|SPSS代写代考|Operational definitions

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|Operational definitions

In designing a study, researchers will determine what variables they are interested in measuring. For example, they might want to measure self-efficacy, student motivation, academic achievement, psychological well-being, racial bias, heterosexism, or any number of other ideas. An important first step in designing good research is to carefully define what those variables mean for the purpose of a given study. When researchers say, for example, they want to measure motivation, they might mean any of several dozen things by that. There are at least four major theories of human motivation, each of which might have a dozen or more constructs within them. A researcher would need to carefully define which theory of motivation they are mobilizing and which variables/constructs within that theory they intend to measure. If a researcher wants to measure racial bias, they will need to define exactly what they mean by racial bias and how they will differentiate various aspects of what might be called bias (implicit bias, discrimination, racialized beliefs, etc.). If a researcher wants to study academic achievement, they might select grade point averages (which are very problematic measures due to variance from school to school and teacher to teacher, along with grade inflation), standardized test scores like SAT or ACT (which are problematic in that they show evidence of racial bias and bias based on income), or a psychological instrument like the Wide-Range Achievement Test (WRAT, which also shows some evidence of cultural bias). However, the research defines the variable and measures it will affect the nature of the results and what they mean. The way that researchers define the variable or construct of interest is referred to as the operational definition. It’s an operational definition because it may not be perfect or permanent, but it is the definition from which the researcher is operating for a given project.

Part of operationally defining a variable involves deciding how it will be measured. Many variables could be measured in multiple ways. In fact, for any given variable, there might be dozens of different measures in common use in the research literature. Each will differ in how the variable is defined, what kinds of questions are asked, and how the ideas are conceptualized. Researchers have a tendency to at times write about variables and measures as if they were interchangeable. They might include statements like,”Self-efficacy was higher in the experimental group” when what they actually mean is that a particular measure for self-efficacy in a particular moment was higher for the experimental group. As we advocate later in this chapter, most researchers will be well served to select existing measures for their variables. But the selection of a way to measure a variable is a part of, and should align with, the operational definition.

统计代写|SPSS代写代考|Random assignment

Another key term in research design is random assignment. In random assignment, everyone in the study sample has an equal probability of ending up in the various experimental groups. For example, in a design where one group gets an experimental treatment and the other group gets a placebo treatment, each participant would have a $50 / 50$ chance of ending up in the experimental vs. control group. This is accomplished by randomly assigning participants to groups. In many modern studies, the random assignment is done by software programs, some of which are built into online survey platforms. Random assignment might also be done by drawing or by placing participants in groups by the order the sign up for the study (e.g.s putting even-numbered sign ups in group 1 and odd in group 2).

Random assignment matters for the kinds of inferences a researcher can draw from a given set of results. By randomly assigning participants to groups, theoretically their background characteristics and other factors are also randomized to groups. So, the only systematic difference between groups will be the treatment or conditions supplied by group membership. As a result, the inferences can be stronger. We would feel more confident that differences between groups are due to group membership (or experimental treatment) when the groups were randomly assigned, because there are theoretically no other systematic differences between the groups. When researchers use intact groups (groups that are not or cannot be randomly assigned), the inferences will be somewhat weaker. For example, if we compare academic achievement at School $\mathrm{A}$, which uses computerized mathematics instruction, vs. School B, which uses traditional mathematics instruction, there might be lots of other differences between the two schools other than whether they use computerized instruction. Perhaps School A also has a higher budget, or students with greater access to resources, or more experienced teachers. It would be harder, given these intact groups, to attribute the difference to instruction type than if students were randomly assigned to instruction type.

Random assignment, though, is not sufficient to establish a causal claim (that a certain variable caused the outcome). Causal claims require robust evidence. For a causal claim to be supported, there must be: (1) A theoretical rationale for why the potential causal variable would cause the outcome; (2) The causal variable must precede the outcome in time (which usually means a longitudinal design); (3) There must be a reliable change in the outcome based on the potential causal variable; (4) All other potential causal variables must be eliminated or controlled (Pedhazur, 1997). Random assignment helps with criterion #4, but the others would also need to be met for a causal claim.
One distinction to be clear about, as it can be confusing for some students, is that random assignment and random sampling (described earlier in this chapter) are two separate processes that are not dependent on one another. Random sampling means everyone in the population has an equal chance of being in the sample. Random assignment means everyone in the sample has an equal chance of being in each group. They both involve randomness but for separate parts of the process.

统计代写|SPSS代写代考|Experimental vs. correlational research

The key difference between experimental and correlational (or observational) research is random assignment. Experimental research involves random assignment, whereas correlational research does not. We have described some of the advantages of experimental research in the kinds of inferences that can be made. Why, then, do researchers do correlational work? The simple answer is that lots of variables researchers might be interested in either cannot or should not be randomly assigned. Some variables should not be ethically or legally randomly assigned. If researchers already know or have strong evidence to believe that a treatment would harm participants, they cannot randomly assign them to that treatment. So, if a researcher wants to examine the effects of smoking tobacco while pregnant on infant brain development, they cannot randomly assign some pregnant women to smoke tobacco, because it causes known harms. Instead, they would likely study infants of women who smoked while pregnant before the study even began. Other variables simply cannot be randomly assigned. If a researcher wants to study gender differences in science, technology, engineering, and mathematics (STEM) degree attainment, the researcher cannot randomly assign participants to gender identities. Although gender identities may be fluid, they cannot be manipulated by the researcher. So, the researcher will study based on existing gender identity groups. That is the only practical approach. But people in different gender identities also have a whole range of other divergent experiences. People are socialized differently based on perceived or self-identified gender identities, they receive different kinds of feedback from parents, peers, and educators, and might be subjected to different kinds of STEM-related experiences. So, it would be difficult to attribute differences in STEM degree attainment to gender, but researchers might try to understand mechanisms that drive differences that occur along gendered lines.

Because many variables cannot or should not be randomly assigned, much of the work in educational and behavioral research is correlational or observational. Causal inferences are still possible, though somewhat harder than with experimental methods. Some of the most important and influential work has been correlational. Our point here is that experimental vs. correlational research is not a hierarchy-neither approach is “better,” but they offer different strengths and opportunities and have different limitations.

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。