discussion replies

include 2 references each

Read the and respond to the four colleagues by noting any discrepancies and/or suggesting alternatives in the levels of measurement and statistical analyses described. Include at least 2 references each response

Precious: Levels of Measurement

Grasping the subtleties of variables and their measurement levels is crucial in nursing research, especially when addressing essential issues such as reducing medication errors among the elderly in long-term care facilities. A straightforward research question is key, stemming directly from the identified research problem. The research problem statement addressed the shortage of effective strategies for reducing medication errors among older individuals in long-term care facilities. The research leads to a specific question: “What are the most successful approaches that can decrease medication errors among older adults in long-term care facilities?”

Variables undoubtedly have crucial roles in research. For this study, independent and dependent variables are both involved. Independent variables are tweaked to observe how they impact the dependent variable, while the researcher measures the dependent variable to determine if its value changes based on alterations in the independent variable (Gray & Grove, 2020). In this research, the independent variables include medication education, interdisciplinary collaboration, and technology usage – all of which can be controlled or manipulated. These interventions aim to examine their effects on the dependent variable – the frequency of medication errors among seniors.

Measuring different variables is all about understanding to what extent they can be quantified or categorized. In order to analyze them effectively, one has to consider their measurement level, which mainly depends on the nature of the variable. For this study, we are looking at medication education, interdisciplinary collaboration, and technology use – and these can best described as ordinal variables. Ordinal measurement generally deals with data that can sorted into categories (StudySmarter, 2023). These categories have an order to them but do not necessarily have evenly spaced intervals. For instance, if we are studying medication education, we might break it down into low, medium, or high levels given to healthcare workers.

In the same way, we can measure cross-disciplinary teamwork on a scale from minimal to moderate to extensive collaboration among various healthcare fields. Similarly, technology usage can be rated based on whether its use in medication management is minimal, moderate, or extensive. Ordinal variables let us rank their categories but do not imply equal spacing between them.

In contrast, the dependent variable of medication errors usually falls within the ratio level of measurements. Ratio data has equal intervals for values that have a valid zero point (zero means zero medication error). Scales highlight variables with differences in measurable values ​​(Zach, 2020). Such grouping of residents in long-term care establishments permits many statistical analyses to be conducted based on the belief that data related to this dimension are discrete numbers capable of comparison, like measuring error rates and proportions in such establishments over a long period. Therefore, classifying each variable has a logical reason at these levels of measurement. Understanding the scope and the extent of influence of these variables lies at the basis of categorizing them.

The ordinal categorization of medical education, inter-professional collaboration, and the use of technologies allow for differentiation from diverse levels of these elements used in care settings, which ultimately facilitates the identification of what is effective. The ordinal variables can be used for ranking purposes, indicating incremental levels of intervention or use. In essence, classifying medication errors as ratio variables allows quantitative analyses to compare error rates in different interventions or populations.

The statistical techniques in analyzing ordinal data include rank-order correlations and non-parametric tests, e.g., the Kruskal-Wallis test. These methods allow one to detect patterns of correlations between various levels or groups of variables. For example, we examine the relationship between higher medication education and reducing medication errors among the elderly. On the other hand, frequency counts are used to analyze ratio variables like the medication errors in this study. Various analytical techniques at a detailed level, like parametric tests such as t-tests and ANOVA, can be used to examine relationships among the variables. Parametric methods provide a more robust test for comparing the two groups (Ali & Bhaskar, 2016). These processes facilitate comparing and analyzing ratio data in various interventions or measures, such as determining whether there are any significant variations in incidences of medication errors across facilities that apply high or low levels of interprofessional collaboration.

While these levels of measurement and analysis have benefits, they can also be challenging. Although ordinal variables are descriptive, they sometimes prove difficult to determine the distance between the given sets of ranks or to ensure that all respondents interpret it in the same way. For instance, the meaning of ‘moderate’ collaboration could differ among people or different facilities. Still, correctly measuring the size of differences among groups could be problematic due to the absence of interval consistency across categories. Conversely, determining medication errors as a ratio variable could be challenging given that accurate data collection and inclusion of other issues influencing the error rate, like workload among staff and patient acuity levels, should be done within a long-term care facility.

Conclusion

Understanding the measurement level for every variable directs choosing a suitable statistical analysis method. Ordinal variables give a better appreciation of intervention effectiveness. In contrast, ratio variables identify distributions but may be incorrect. Understanding such nuances, therefore, informs a comprehensive analysis, leading to accurate implications for nursing research, especially about improving care and safety for elderly patients in long-term hospitals.

Sandra: It is important when researching to make sure the selected sample is representative of the entire population (Kang, 2021). If it were feasible to research an entire population of interest, there would be more accurate findings as a result. But in most cases, it is impractical to research an entire population. Therefore, to analyze data from a selected sample, the sample must be an accurate estimate of the entire population so that the appropriate sample size can answer the research question.

Research Question

           
 My research statement is on nurse burnout. My research question is,
 in acute care nursing, how does a demanding workload compared to poor staffing ratios influence burnout?

Independent and dependent variables

            The independent variables are increased patient workload, poor staffing ratios, and high employee turnover (ANA, 2023; Kelly et al., 2021). The dependent variables are burnout, mental and physical exhaustion, reduced efficacy in the workplace, depersonalization, cynicism, and decreased personal accomplishments (Dall’Ora et al., 2020).

Levels of measurement

           
 The level of measurement for the dependent variable (burnout, physical exhaustion, depersonalization, cynicism, and reduced efficacy in the workplace) and the independent variable (burnout, physical exhaustion, and depersonalization) is the interval
 scale (Portland State University, n.d.).

Classification of each variable

        The independent
 variables of increased patient workload, poor staffing ratios, and high employee turnover give us information about the issue that contributes to nurse burnout, leading to feelings of depersonalization, physical exhaustion, cynicism, and decreased personal accomplishment. In other words, poor staffing ratios and an increased patient workload can lead to physical exhaustion. Also, high employee turnover is a predictive factor for nurse burnout. Researchers measure the interval using a numerical scale that has equal intervals between adjacent values (Stevens, 2023). Descriptive statistics can be used to analyze the interval scale. A poor staffing ratio and increased patient workload correlate with burnout, while high employee turnover or an increased patient workload correlate with feelings of depersonalization. Furthermore, the interval scale has no true zero point, and there is an equal distance between two consecutive scale points (Portland State University, n.d.).

 

Analyzing data related to each variable

            The interval scale is more effective than the nominal or ordinal scale and allows one to categorize and label variables and rank categories in order. It has a known equal interval but no true meaningful zero (Stevens, 2023). The nominal and ordinal scales are qualitative, while the interval scale is quantitative and straightforward (Bhat, 2023). Lastly, the interval scale is a preferred scale in statistics and scholarly research because one can assign a numerical value to an arbitrary assessment such as sentiments and feelings.

Researchers commonly use questions that can be measured on an interval scale, such as a Likert scale, to analyze variables in research studies. For example, to receive answers to my research question (In acute care nursing, how does a demanding workload compared to poor staffing ratios influence nurse burnout?) To obtain interval data for my research question (In acute care nursing, how does a demanding workload compared to poor staffing ratios influence nurse burnout?), the feedback options should be restricted to variables that can be assigned a numerical value, ensuring that the difference between the two variables (increased workload = burnout) is equal (Bhat, 2023).

            To answer the research question, we will use a survey to ensure that the mentioned variables meet the criteria of interval measurement and provide results (Bhat, 2023). Researchers commonly use a 5-point Likert scale as an interval scale question. Each emotion (depersonalization, burnout, physical exhaustion, and cynicism) is assigned a number, and the variable ranges from 1 (highly agree) to 5 (highly disagree). Thus, the interval scale gives the investigator the ability to quantify and differentiate between options so that the feedback can contribute to meaningful results.

            An advantage that I may encounter in my statistical analysis of each variable is being able to measure the emotions that the nurses feel with a numerical value. The Likert scale will make it possible to survey the nurses to get an accurate response. Also, the interval scale measures the frequency distribution, such as the mean, median, mode, standard deviation, and variance, with a known spaced interval (Stevens, 2023). The only disadvantage may be that when it. comes to analyzing results, there is no true zero using an interval scale, except using the ratio scale.

Iretioluwa: One of the key system level issues that surround the health care system is the shortage of nurses (Al Zamel et al., 2020). This gives rise to the management or leadership issue of nurses’ retention. The problem of nurses’ retention is a major problem of health care leaders, as qualified nurses are needed to achieve the end goal of positive patient outcomes. There is no easy solution to the problem of nurses’ retention. The solution or the strategy to have a high level of nurses’ retention will depend on the overall organizational culture. Nurses’ burnout is a major reason for the issue of nurses’ retention. 

Research Question:

What is the impact of nurse burnout at individual levels on nurses’ retention?

Independent variables are factors that influence outcomes. Dependent variables are measures of the outcomes themselves. For this research 

Independent variable: Nurses burnout

This would be a Ordinal variable as nurses can put burnout as low, medium and high. 

Dependent variable: Nurses’ retention

This would be also be an ordinal variable and can be defined as percentage value. 

Researchers use hypothesis to determine what is by chance or a plausible explanation through data analysis. There needs to be at least one hypothesis in a research study, but the researchers can have as many as they deem necessary (Gray & Grove, 2020). Generally, researchers use a null and alternative hypothesis to direct a study. The null and alternative hypotheses are opposite from one another. The null hypothesis is attempting to show a lack of relationship between the variables, while the alternative hypothesis is attempting to show that there is a relationship between the variables. The goal of the research study is to reject or fail to reject the null hypothesis. In order for the alternative hypothesis to be accepted, the null hypothesis must first be rejected. The researchers will decide whether to reject the null based on the probability of the results being accurate (Gravel et al., 2021). I say probability because research cannot prove a hypothesis will be true every time. Researchers are able to reject the null hypothesis and accept the alternative hypothesis if a relationship or an effect is shown. Therefore meaning, if no effect or relationship is shown then the study will have failed to reject the null hypothesis.

Evette: Research Question and Variables

Research Question

How do patient knowledge levels, technology use, healthcare provider-patient communication, and socio-demographic characteristics affect medication adherence in treating chronic illnesses?

Variables

In week two, I identified my problem statement to evaluate medication adherence among patients with chronic diseases and its correlations with various parameters, including how providers and patients communicate, technology use, and patient education. Gray and Grove (2020) say that the independent variables (IVs) and dependent variables (DVs) must be identified when proposing research to help develop a complete research question. Patient knowledge level, socio-demographic characteristics, provider-patient contact, and technology usage are IVs that potentially impact medication adherence. Meanwhile, the DV, medication adherence, is measured via self-reports, routine blood pressure, and glucose monitoring.

Levels of Measurement and Rationale

Measuring patient medication knowledge entails examining patient’s understanding of their dosage, whether they know the frequency of taking the prescribed drugs, and the significance of the medications in addressing their chronic conditions. As such, this variable is classified as an ordinal variable. It may be examined on an ordinal scale, allowing patients to be classified depending on their level of comprehension. For example, responses might vary from “low,” indicating little knowledge, to “moderate,” showing an acceptable level of awareness, and “high,” meaning a thorough understanding of what their medication intends to treat. This is well demonstrated in the Gholami et al. (2020) study, where they assess pain levels among children based on such levels.

Income and other socioeconomic indicators are made up of ordered categories. Therefore, it meets the standard for ordinal categorization. Income levels align hierarchically, and this allows a researcher to classify them as “low income,” “middle income,” and “high income.”

The evaluation of the quality of provider-patient communication is also ordinal. The use of descriptions such as “poor,” “fair,” and “good,” as well as the use of Likert scales with several answer alternatives, suggests an orderly progression (Gray & Grove, 2020). Similarly, when defining degrees of technology use as “rarely,” “sometimes,” or “frequently,” it shows an orderly progression, making ordinal categorization suitable. Their categories imply escalating levels of technological involvement or consumption, although they may not always represent equal intervals between them.

The DV is medication adherence. It is monitored indirectly by self-reports and direct monitoring of physiological indicators. For example, blood pressure and blood glucose levels are classified as ratio variables because their numerical values have a valid zero point.

Analyzing Data and Considerations

These variables are ordinal, so non-parametric statistical techniques like ordered logistic regression can be applied in the research. These techniques support ranked categories without presuming that the intervals between them are equal. The advantage of this is that it allows the researchers to assess patterns or connections between different levels. For example, reclassifying patient knowledge as an ordinal variable facilitates its alignment with the nature of the variable and how it is meant to be measured. The researcher will be able to evaluate better how the variable influences adherence to treatment for a chronic disease like diabetes (Gray & Grove, 2020).

Advantages and Challenges

The levels of measurement provide effective statistical analyses. This way they help to better understand how variables interact with one another. Gray and Grove (2020) say that ordinal variables influence mathematical operations and statistical interpretations. This is because they lack precise equal intervals, making them unsuitable for ranking and comparing categories. The variables also present challenges, such as restrictions on statistical tests like regression analysis because of the nature of the data.

Conclusion
 

In essence, to design appropriate statistical analyses and obtain meaningful insights into the variables under study (in this case, medication adherence and associated factors among patients with chronic illnesses), it is important to recognize the levels of measurement for each variable. This can be achieved by considering the advantages and disadvantages of various measurement levels, as discussed in this paper.

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