Assignment Instructions:
Read a selection of your colleagues’ responses by noting any discrepancies and/or suggesting alternatives in the levels of measurement and statistical analyses described.
NOTE: Each Colleague had to initially post a research question and describe the independent and dependent variables. Then, identify the level of measurement of both your independent and dependent variables. Provide a brief rationale for your classification of each variable. Be specific. Include any advantages or challenges that you might encounter in your statistical analysis of each variable and explain why.
Also, PLEASE MAKE SURE THE REFERENCES FOLLOWS EACH COLLEAGUE RESPONSE. DO NOT put the references together. APA 7th edition references, no older than 5 years! Each Colleague needs 2 References each, making it a total of 4 references.
Colleague #1
Research Question: How does the frequency of dialysis treatments (thrice weekly vs. daily) affect the quality of life and mental health outcomes among patients with end-stage renal disease (ESRD)?
The independent variable in this study is the frequency of dialysis treatments, categorized as thrice weekly or daily. This variable is measured at the nominal level because it involves categorizing dialysis regimens into distinct, non-ordered groups (Martin et al., 2020). The dependent variables are the quality of life, measured by the Kidney Disease Quality of Life Short Form (KDQOL-SF) questionnaire, and mental health outcomes, assessed using the Hospital Anxiety and Depression Scale (HADS). KDQOL-SF and HADS generate interval-level data, as they produce scores representing the degree of quality of life and mental health, with equal intervals between values (Yonata et al., 2020).
Analyzing nominal-level data for the independent variable involves categorical data analysis techniques, such as chi-square tests or logistic regression, which are appropriate for comparing proportions between groups (Martin et al., 2020). The interval-level data for the dependent variables allow for more complex statistical analyses, including t-tests, ANOVA, or regression analysis, which can identify mean differences and relationships between variables (Yonata et al., 2020).
One advantage of using interval-level data is the ability to apply parametric statistical tests, which are generally more powerful and provide more detailed information about the relationships between variables. However, challenges include ensuring that the data meet the assumptions of these tests, such as normality and homogeneity of variance. In contrast, nominal data analysis is less complex but provides less detailed information. Ensuring accurate and consistent measurement of the KDQOL-SF and HADS is critical to the validity of the study’s findings (Yonata et al., 2020).
Collaegue #2
Research Question
How does the implementation of psychiatric advance directives (PADs) in psychiatric clinics or community health centers affect mental health outcomes for patients with mental health conditions?
Variables
The independent variable in this study is the implementation of Psychiatric Advance Directives (PADs). This variable indicates whether or not PADs are implemented in psychiatric clinics or community health centers. It is measured at the nominal level, categorizing the data into “Implemented” and “Not Implemented.” These categories are distinct and do not have an inherent order (Wooditch et al., 2021).
The dependent variables are divided into two main categories: mental health outcomes and mental health care providers’ knowledge and attitudes.
Mental Health Outcomes: Mental health outcomes are assessed using various standardized scales, including: Hospital Anxiety and Depression Scale (HADS), Beck Depression Inventory (BDI), Dissociative Experiences Scale, Goldberg Bipolar Spectrum Screening Questionnaire, Hamilton Anxiety Scale (HAM-A) and Schizophrenia Test and Early Psychosis Indicator (STEP). According to Wu et al., (2021), these scales provide scores that have meaningful intervals between values, making them interval-level measurements. For example, a score of 20 on the HADS is twice that of a score of 10, indicating a quantitative difference. While these scales often assume that the distance between scores is consistent, they may not have a true zero point, thus categorizing them as interval rather than ratio measurements.
Mental Health Care Providers’ Knowledge and Attitudes: This variable is assessed via questionnaires that often use Likert scales. These scales provide ordered categories (e.g., strongly agree, agree, neutral, disagree, strongly disagree) without assuming equal intervals between points, making this an ordinal level of measurement (Jebb et al., 2021).
Considerations for Analyzing Data
Independent Variable: Implementation of PADs
One advantage of this nominal variable is the clear comparison it allows between groups with and without PADs implementation. It is straightforward to categorize and include in various statistical analyses, such as chi-square tests for association. However, a challenge with this binary variable is that it only provides a yes/no implementation status, lacking depth in terms of the extent or quality of implementation.
Dependent Variables
Mental Health Outcomes: These outcomes provide rich, quantitative data for analysis. The use of well-validated scales enhances the reliability and validity of the findings. However, some challenges include the assumption of equal intervals between scale points, which may not always be accurate, and the need for appropriate statistical techniques such as t-tests, ANOVA, or regression, considering the properties of the scales.
Mental Health Care Providers’ Knowledge and Attitudes: Ordinal data from Likert scale questionnaires capture the order of responses, providing more detail than nominal data. They can be analyzed using non-parametric tests if the assumptions of parametric tests are not met. However, treating Likert scale data as interval data for convenience may not be strictly accurate. Additionally, ordinal data do not provide information about the magnitude of differences between categories.
Statistical Analysis Considerations
For the independent variable, which is nominal, analysis will involve chi-square tests for independence or logistic regression if examining the probability of outcomes. For the dependent variables, interval data will be analyzed using parametric tests such as t-tests, ANOVA, and linear regression, assuming a normal distribution and equal intervals. Ordinal data will be analyzed using non-parametric tests like the Mann-Whitney U test or Kruskal-Wallis test, or ordinal logistic regression for Likert scale data (Peck et al., 2020).
Summary
In summary, this study involves a nominal independent variable (Implementation of PADs) and a mix of interval (Mental Health Outcomes) and ordinal (Mental Health Care Providers’ Knowledge and Attitudes) dependent variables. Addressing these considerations will enable a robust analysis of how the implementation of PADs affects mental health outcomes and the attitudes and knowledge of mental health care providers.
