This is due on friday 10/26/2018. Find attached one classmates response to be submitted on a separate file.

Discussion # 1

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D1.1 Variables

What kind of 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 casual inferences be more questionable?

            In order to infer cause the researcher must use an active variable. This is because an active variable is both random and experimental, providing the researcher with a conduit to infer change or establish a reactional effect has taken place due to the active variable and not a pre-existing variable. The researcher should not automatically conclude change or effect is caused by an active variable though, when identifying the main effect, the researcher should simply focus on the active variable and not the attribute variable to support their inference.

D1.2 Research Questions

Compare and contrast associational, difference and descriptive types of research questions.

            Associational, difference, and descriptive research questions are all types of quantitative research questions. Difference research questions focus on identifying differences between dependent variables. The associational research questions attempt to identify a correlation, or positive relationship between variables. According to Morgan et al (2013) descriptive research questions do not attempt to make inferences through statistical analysis, but the purpose of this research question is to present a summary of the data collected.

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:

Will students who study more often be more successful in college?

b.      Write a difference question:

What is the difference in online doctoral persistence between males and females?

c.       Write a descriptive question:

What percentage of doctoral students are veterans?

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?

            Quantitative research is an extremely new concept to me, so honestly it is hard for me to identify any additional coding aspects to the current learning material. One area that has not been immediately discussed, at least not yet, is ethics in coding. It would be very easy to manipulate the input of data collected, presenting an unauthentic statistical analysis to support a particular agenda, or beliefs, however as researchers we are called to a high standard of integrity.

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 problem and why?

            The only other problem that I discovered that was not discussed in the text was interview ID 7. Instead of circling a gender, they crossed one off. In the context of the questionnaire I think as researchers we can reasonably infer this to mean the participant is a male. Going forward, I would include this as a rule when coding, that when an “X” is used instead of circling the answer, we can infer the participant meant the same as if they were to circle the answer.

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 them after entering them?

            Reviewing your data prior to entering it will allow the researcher to identify anomalies in the data collected. The researcher can then establish rules to apply to the research going forward. This will facilitate a streamlined approach to similar anomalies, contributing to the validity and reliability of the quantitative research. Watson (2015) indicated that reliability in quantitative research is measured by the consistency of presenting the data collected. To do this prior to entering the data would involve the researcher reviewing the data collected, looking for answers that don’t align with the format of the questionnaire, or for unique answers to the questions. After entering the data, the researcher should again review their work for multiple reasons. The first, the researchers should check for simple data entry errors that might have occurred during the transferring of information. The second, ensuring the codebook created was followed for all participants, contributing to the validity and integrity of the statistical analyses from the researcher.      

References

Morgan, G., Leech, N., Gloeckner, G., & Barrett, K. (2013). IBM SPSS For Introductory

Statistics (5th ed.). New York, NY: Routledge.

Watson, R. (2015). Quantitative research. Nursing Standard,29(31), 44-48.

doi:10.7748/ns.29.31.44.e8681

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