The Martin & Martin (2014) study on the Healthy Amistad program utilized several measurement levels and data collection techniques. They gathered nominal data through diagnostic categories to identify participants with serious mental illness. Ratio data was collected from clinical records such as biometric measurements like weight and blood pressure. Interval data was obtained through several self-report surveys, including questions on health behaviors, emergency room visits, calling crisis lines, and life satisfaction.
I would also use medical records and clinician diagnoses for my made-up mental health study to get important nominal and ratio data about the sample population. Standardized self-report scales on symptoms, functioning, and well-being wellbeing would give me essential interval data. For example, I would use the PHQ-9 for depression, the GAD-7 for anxiety, the PROMIS emotional distress scale, the WHO-5 wellbeing index, and the PERMA multidimensional wellbeing scale.
Additionally, I would collect qualitative data through participant interviews, similar to Martin & Martin’s interviews, gathering insights into health behaviors and perspectives. Interviews allow deeper investigation of subjective experiences. However, I would only partially rely on self-reported behaviors, as in the Healthy Amistad surveys, since these can be biased or inaccurate. Instead, I would combine self-reports with clinician assessments, biometric data, and direct behavioral observations to measure outcomes objectively. This mixed methods approach integrates standardized quantitative instruments with qualitative insights, balancing reliable data collection with exploring personal narratives. Diagnostic criteria, clinical records, validated scales, interviews, and behavioral observations would provide a robust and comprehensive measurement of mental health interventions.
My personal bathroom scale is moderately reliable in consistently measuring my weight daily but may need more validity than the doctor’s office scale. A bathroom scale is usually reliable, meaning if you step on and off it a few times in a row, it will give you pretty much the same reading each time. For repeated measurements, it is pretty consistent. However, this doesn’t mean it is perfectly accurate. Doctor’s scales often use advanced technology like digital sensors and algorithms to average multiple weight readings. This increases reliability and validity. Reliability refers to how consistent measurements are on repeated trials. My scale usually gives the exact weight when I repeatedly step on and off it. However, its validity or accuracy in measuring accurate weight must be investigated. There are often slight differences between my home reading and the doctor’s office number.
Several factors can contribute to measurement error and reduced validity with my bathroom scale. It may not be adequately calibrated or have a mechanical defect, throwing off accuracy. The scale’s position on the floor, interference from carpeting, or how I stand on it can also impact the reading. Finally, my scale provides only a single weight data point, while the doctor’s scale takes multiple averaged measurements. This makes the medical scale more reliable and valid.
It is tempting to adjust my scale to match my biases about my ideal or expected weight. However, this needs to be revised to collect and analyze data properly. Good science depends on measurements that reflect the empirical truth, not our assumptions or personal wishes. Even if results don’t align with my beliefs or preferences, ethical principles demand accurately reporting findings. Otherwise, it’s pseudo-science at best. While my weight matters to me personally, I must separate my ego from the data when conducting research. Objective, precise measurement, and responsible analysis are essential for generating valid conclusions in any scientific field. My bathroom scale may have limitations, but data collection and reporting integrity should be absolute.
References
Martin, M. B., & Martin, S. L. (2014). Healthy Amistad: improving the health of people with severe mental illness. Issues in Mental Health Nursing, 35(10), 791-795.
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