NURS 8200 Discussion 1: Levels of Measurement

Grove, 2020; Polit & Beck, 2020). There are two types of variables, independent and dependent variables (Gray & Grove, 2020). My research question is “Can implementing an educational program promote positive nursing practice change by increasing staff usage of the PHQ-9 questionnaire for screening depression inpatients in primary care settings?”. Using my research question, the independent variables are an educational program, and not implementing the educational program. Independent variables are research measures that are manipulated by the researcher during a study (Gray & Grove, 2020; Polit & Beck, 2020). As the researcher, I will be able to control the implementation of the educational program. On the other hand, the dependent variables in my research question include positive nursing practice change and staff usage of the PHQ-9 questionnaire for screening depression. Dependent variables are outcome measures that are subject to change depending on the researcher’s manipulation of the independent variables a research study (Gray& Grove, 2020; Polit & Beck, 2020). The outcomes of positive nursing practice change and staff usage of the PHQ-9 questionnaire for screening depression will be dependent on the implementation process of the educational program.

There are four levels of measurement for the varying variables in a research study, nominal, ordinal, interval and ratio intervals (Gray & Grove, 2020). The level of measurement for the positive nursing practice change variable is the ordinal level because the outcome will be

measured by either “positive change” or “no change” (Gray & Grove, 2020; Polit & Beck,2020). The level of measurement for the staff usage of the PHQ-9 questionnaire for screening depression variable is the interval level because I will use a test score to categorize the

staff usage of the PHQ-9 questionnaire for screening depression in equal intervals (Gray &rove, 2020; Polit & Beck, 2020).

The advantage of using the ordinal measurement for the positive nursing practice change.

variable is the ease of collating and categorizing data for extensive statistical analysis (Gray &

Grove, 2020; Polit & Beck, 2020). However, ordinal level of measurement uses surveys and

questionnaires for data collection predisposing a risk for narrow responses that may create

bias (Gray & Grove, 2020; Polit & Beck, 2020). On the other hand, using the interval level for

measuring the staff usage of the PHQ-9 questionnaire for screening depression is the ability to

assess a wide scope of data and categorize the data in equal intervals (Gray & Grove, 2020; Polit

& Beck, 2020). Moreover, the mean and standard deviation, and range of the data can be

obtained (Gray & Grove, 2020; Polit & Beck, 2020). However, it lacks an absolute zero (Gray &

Grove, 2020; Polit & Beck, 2020).

NURS 8200 Discussion 1: Levels of Measurement

Research Question

In sleep apnea patients (P), how does the treatment of OSA with CPAP (I) compared with no treatments (C) reduce the risk of cardiovascular diseases (O)?

Independent and Dependent Variables

Independent variable: Treatment of OSA with CPAP. It does not vary. It is not dependent upon other variables

Dependent variable: Cardiovascular disease. Cardiovascular disease is the dependent variable because not everyone has this disease. Its value depends on changes in the independent

Level of Measurement of Both the Independent and Dependent variables

Measurement involves assigning numbers to qualities of people or objects to designate the quantity of the attribute. There are four different levels of measurement: nominal, ordinal, interval, and ratio (Polit & Beck, 2020).

Ordinal measurement will be used for my independent variable. Rationale: This will be the best measurement to rate patients’ improvement using CPAP from 1 to 10. The ordinal measurement will be a great tool when evaluating the Epworth Sleepiness Scale ( ESS) for my sleep apnea patients to compare the ESS score before the use of CPAP and while using Something measured on an ordinal scale does have an evaluative connotation (Bond C.M, 2019).

Interval measurement will be used for my dependent variable. I will use the interval measurement to get greater analytic flexibility, more robust statistical options, and a more significant amount of information than at the lower levels (Polit & Beck, 2020).

Discuss considerations of analyzing data related to each variable based on its level of measurement. What are the advantages, or disadvantages, of the levels of the variables of measurements?

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When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is essential to understand the

source of the numbers. The term variable defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values, to help describe the character of an individual study participant. For example, the variable “sex” would have values of “male.” and “female” (Bond C.M, 2019).

An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or permanently). Nominal-level Furthermore, ordinal-level variables are also referred to as categorical variables because each category in the variable can be separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive types also having meaning. Age, weight, and blood glucose can be considered interval variables and ratio variables because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories (Simpson, 2018).

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage because we lose information when variables are reduced or aggregated, a common practice that is not recommended. For example, if age is lowered from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years), we lose the ability to make comparisons across the entire age range and introduce error into the data analysis (Simpson, 2018).

References

Bond C.M. ( 2019). The research jigsaw: how to get started. Can J Hosp Pharm., 67(1):28–30.

Simpson S. H. (2018). Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study. The Canadian journal of hospital pharmacy, 68(4), 311–317. https://doi.org/10.4212/cjhp.v68i4.1471

Polit, D. F., & Beck, C. T. (2020). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.

Your research question, “Can an improvement in ethical and compassionate practices from healthcare workers reduce relapse and death for dual diagnosis patients in addiction treatment centers?” is well-thought-out and addresses a crucial aspect of mental health and substance abuse treatment. You’ve correctly identified the independent variable (improvement of ethical and compassionate practices) and the dependent variable (reduction in relapse and death). Your approach to making the independent variable measurable through specific actions like one-to-one attention and assurance of patient rights is commendable. This specificity level will help in accurately assessing the impact of these practices.

You’re right in emphasizing the importance of accurate and reliable data collection. Ensuring the validity and reliability of the measurements and considering the potential challenges in data recording are essential steps in research. Your insight into the need to stay focused on specific variables to obtain valid results is astute and shows a deep understanding of the research process.

A follow-up question: How do you plan to address potential confounding factors that could influence the outcomes of dual-diagnosis patients, such as socioeconomic status or access to healthcare resources?