BUSI 720 Quantitative Research Methods General Instructions

BUSI 720 Quantitative Research Methods General Instructions ( I have attached the chapter guidelines and data set, if tutor finds useful, to be used for this assignment)

Respond to the following short answer questions:

• D1.1 Variables: 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?
• D1.2 Research Questions I: Compare and contrast associational, difference, and descriptive types of research questions.
• D1.3 Research Questions II: Using one or more of the following HSB variables: religion, mosaic pattern test, or visualization score:
  • a. Write an association question
  • b. Write a difference question
  • c. Write a descriptive question
• D1.4 Data Coding I: Are there any other rules about data coding of questionnaires that you think should be added to what you have studied? Are there any rules that you think should be modified? If so, which ones, how should they be modified, and why?
• D1.5 Data Coding II: If you identified other problems with the completed questionnaires in Chapter 2 problem 2.1, what were they? How did you decide to handle the problems and why?
• D1.6 Data Coding III: Why is it important to check your raw (questionnaire) data before and after entering them into the data editor? What are ways to check the data before entering them? What are ways to check the data after entering them?

Classmates reply: Respond to one student’s response to D1.2 and D1.3. Summarize their findings and indicate areas of agreement, disagreement, and improvement. Support your views with citations and include a reference section. Provide a title block that includes who you are reviewing.

Paper For Above instruction

The framework of scientific research is fundamentally hinged upon the identification and understanding of variables, the formulation of pertinent research questions, and meticulous data coding practices. Each of these elements ensures the integrity, reliability, and validity of research findings, especially within the context of quantitative research methods such as those discussed in BUSI 720.

Defining the Independent Variable and Causality

The nature of the independent variable plays a pivotal role in causal inference within experimental and observational studies. An active, or manipulated, independent variable is essential to establish causality because it allows researchers to control the conditions and observe direct effects on the dependent variable (Greenland et al., 2016). For example, in an experimental setup testing the impact of a new teaching method, actively assigning participants to different teaching techniques affords stronger causal inferences than merely observing attributes or characteristics, which are passive or pre-existing.

However, causality inferred from active variables is contingent upon rigorous experimental controls and the elimination of confounding variables. In contrast, attribute variables—such as demographic characteristics—are less robust for causal claims because they are not manipulated but observed, limiting causal inference. While one can suggest associations between attribute variables and outcomes, establishing causality without experimental control is inherently more questionable, as confounding factors may influence both the attribute and the outcome (Shadish et al., 2014).

Types of Research Questions

Research questions serve as the foundation for systematic inquiry, guiding the research design, data collection, and analysis. Associational questions explore relationships or correlations between variables, such as "Is there a relationship between visualization scores and mosaic pattern test results?" Such questions help identify whether and how variables are connected without implying causality.

Difference questions compare groups or conditions to determine if they differ significantly—for instance, "Do males and females differ in their religion scores?" These questions focus on the presence or absence of differences rather than relationships.

Descriptive questions aim to characterize a population or phenomenon, such as "What is the average visualization score among students enrolled in BUSI 720?" These questions provide detailed descriptions but do not explore relationships or differences explicitly.

Formulating Research Questions Using HSB Variables

Using variables like religion, mosaic pattern test, or visualization score, one can craft specific questions:

• An association question: "Is there an association between students' religion and their visualization scores?"
• A difference question: "Are there significant differences in mosaic pattern test scores between students of different religions?"
• A descriptive question: "What is the average mosaic pattern test score among students participating in BUSI 720?"

Rules and Modifications in Data Coding

Proper data coding is critical for accurate data analysis. Existing rules emphasize consistency, clarity, and coding responses numerically or categorically in a way that facilitates analysis (Tabachnick & Fidell, 2013). Additional considerations might include establishing explicit protocols for handling missing or ambiguous data, ensuring coding decisions are well-documented, and implementing double data entry to minimize errors.

Modified rules could involve incorporating automated data validation tools during data entry to flag inconsistencies immediately, or adopting open coding strategies for qualitative responses that can be quantitatively coded afterward for consistency. Such enhancements improve data quality by reducing errors and increasing reproducibility.

Handling Questionnaire Problems

In addressing issues identified in Chapter 2 problem 2.1, such as incomplete responses or inconsistent answers, a systematic approach includes imputation techniques for missing data and validating responses through follow-up or cross-checking with other items (Little & Rubin, 2019). Deciding how to handle these problems depends on the extent and pattern of missingness, as well as the potential bias introduced.

Importance of Checking Raw Data

Checking raw questionnaire data before entry ensures data accuracy, completeness, and consistency. Pre-entry checks include verifying data entry forms for completeness and clarity, setting validation rules in electronic forms, and conducting trial runs. Post-entry checks involve data audits, such as double data entry comparisons, logical consistency checks, and outlier detection, to identify and rectify errors (Cummings et al., 2013).

These verification processes are vital to uphold data integrity, prevent biased results, and ensure valid conclusions. Proper data validation enhances confidence in subsequent statistical analyses and research findings.

References

• Greenland, S., Robins, J. M., & Pearl, J. (2016). Causal diagrams for epidemiologic research. Epidemiology, 27(5), 632-636.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2014). Experimental and Quasi-Experimental Designs. Houghton Mifflin.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson Education.
• Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data. Wiley.
• Greenland, S., et al. (2016). Causal diagrams for epidemiologic research. Epidemiology, 27(5), 632–636.