Read Morgan Leech Gloeckner Barrett Chapters 1 And 2 Appendi

Read Morgan Leech Gloeckner Barrett Chapters 1 2 Appendices

Read: Morgan, Leech, Gloeckner, & Barrett: Chapters 1 – 2, Appendices A – B.

Read: Keller: Introduction – Part I.

Watch: Research Questions Hypothesis and Variables Discussion.

Assignment Instructions: The student will complete short-answer discussions in this course. The student will post short answers to the prompted questions in the thread for each short-answer discussion. The answers must demonstrate course-related knowledge and support their assertions with scholarly citations in the latest APA format. Minimum word count for all short answers cumulatively is 200 words. For each thread, the student must include a title block with your name, class title, date, and the discussion forum number; write the question number and the question title as a level one heading (e.g., D1.1 Variables) and then provide your response; use Level Two headings for multi-part questions (e.g., D1.1 & D1.1.a, D1.1.b, etc.), and include a reference section. Respond to the following short answer questions from Chapter 1 of the Morgan, Leech, Gloeckner, & Barrett textbook: This introduction to the research problem and questions raised by the HSB dataset should help make the assignments meaningful, and it should provide a guide and some examples for your own research. Interpretation Questions D1.1.Compare the terms active independent variable and attribute independent variable. What are the similarities and differences? D1.2.What kind of independent variable (active or attribute) is necessary to infer cause? Can one always infer cause from this type of independent variable? If so, why? If not, when can one infer cause and when might causal inferences be more questionable? D1.3.What is the difference between the independent variable and the dependent variable? D1.4.Compare and contrast associational, difference, and descriptive types of research questions. D1.5.Write a research question and a corresponding hypothesis regarding variables of interest to you but not in the HSB dataset. Is it an associational, difference, or descriptive question? D1.6. Using one or more of the following HSB variables, religion, mosaic pattern test, and visualization score (a.)Write an associational question. (b.)Write a difference question. (c.)Write a descriptive question.

Paper For Above instruction

Introduction

The foundational understanding of research variables is crucial for designing and interpreting scientific studies. The concepts of independent and dependent variables underpin the causal inferences and descriptive insights that drive knowledge advancement. Clarifying the distinctions and relationships among various types of variables such as active and attribute independent variables is essential for researchers aiming to establish cause-effect relationships, describe phenomena, or explore associations among variables. This paper addresses key questions regarding the nature of independent variables, their role in causal inference, and the types of research questions that are formulated based on these variables, with illustrative examples rooted in social science research contexts.

Comparison of Active and Attribute Independent Variables

Active independent variables are variables that researchers manipulate or control directly within experimental settings to observe their effects on dependent variables. An example is assigning participants to different treatment conditions in a clinical trial. Conversely, attribute independent variables are characteristics or traits inherent to participants or subjects that are not manipulated by the researcher, such as age, gender, or ethnicity. The primary similarity between active and attribute independent variables is that both are independent in the sense that they are presumed to influence or be associated with the dependent variable. The key difference is that active variables are intentionally manipulated to assess causal effects, while attribute variables are inherent qualities that can be examined for associations but not manipulated within the study context (Shadish, Cook, & Campbell, 2002).

Necessary Conditions for Inferring Causality

Active independent variables are typically necessary to infer causality because their manipulation under controlled experimental conditions reduces confounding factors and enhances internal validity (Cook & Campbell, 1979). When an active variable is manipulated randomly, causal inference is more justifiable since it minimizes bias. However, in observational studies where attribute variables are examined, causal inferences are more complex and less definitive because correlation does not imply causation, and confounding variables may be uncontrolled (Hill, 1965). Causality can often be inferred with experimental manipulation and random assignment but is more questionable in non-experimental, observational studies where the temporal and causal order of variables may be ambiguous (Shadish et al., 2002).

Differences Between Independent and Dependent Variables

The independent variable is the factor that the researcher manipulates or considers as the predictor or explanatory variable. It is hypothesized to influence or predict the outcome variable. The dependent variable is the outcome or response that is measured to assess the effect of the independent variable. In essence, the independent variable is the presumed cause, while the dependent variable is the effect or outcome that is observed and analyzed to determine the impact of the independent variable (Creswell, 2014).

Types of Research Questions

Associational research questions seek to identify whether a relationship or association exists between variables without implying causality; for example, "Is there a correlation between daily exercise and mental health?" Difference research questions compare groups or conditions to identify differences, such as "Do males and females differ in their levels of social media use?" Descriptive questions aim to depict or characterize variables or phenomena, such as "What is the average visualization score among college students?" These three types differ fundamentally in purpose and analytical approach: associational questions focus on relationships, difference questions on comparisons, and descriptive questions on characterization (Kerlinger & Lee, 2000).

Research Example with New Variables

Suppose I am interested in examining the relationship between extracurricular involvement and academic performance among high school students. An example of an associational question would be: "Is there a correlation between hours spent on extracurricular activities and GPA?" The corresponding hypothesis might be: "Higher hours spent on extracurricular activities are associated with higher GPA scores." This is an associational question because it explores a relationship between two variables without implying causality.

Using HSB Variables for Different Types of Questions

a. Associational Question

Is there a relationship between students' religion and their mosaic pattern test scores? For example: "Does religious affiliation correlate with mosaic pattern test scores among high school students?"

b. Difference Question

Do students with high visualization scores differ in their average scores from those with low visualization scores? For example: "Are students with high visualization scores significantly different in their academic achievement compared to students with low visualization scores?"

c. Descriptive Question

What is the average score on the mosaic pattern test among high school students? For example: "What is the mean mosaic pattern test score among students in the school?"

Conclusion

Understanding the distinctions among types of variables and research questions enhances the rigor and clarity of social science research. Differentiating between active and attribute independent variables helps in designing proper experimental and observational studies, while understanding the nature of research questions guides appropriate analysis and interpretation. The examples provided illustrate how these concepts are applied to real-world research scenarios, emphasizing the importance of clarity in hypothesis formulation and question design.

References

• Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
• Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.
• Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research. Harcourt College Publishers.
• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.