Discussion
13593In chapter two of your text, you read about dependent and independent variables. As a simplistic refresher, an independent variable is often the presumed cause of the dependent variable. The dependent variable is the outcome that the researchers are trying to understand, provide statistics about, or explain in general.
Make sure that you think through these definitions each time you evaluate a work of research. Every research design is a bit different. So, something that was an independent variable in one study might be a dependent variable in the next.
In chapter 13, Understanding Statistical Analysis of Quantitative Data, we find that quantitative variables can be further defined as to levels of measurement:
Nominal measurement uses numbers for simple categorization. It is the lowest level of measurement. These numbers generally do not have a quantitative meaning and cannot be calculated mathematically. Often nominal measurements are numbers that we use to categorize demographic information. For example, when putting demographic information into a spreadsheet, we might assign a number to the patient’s gender (1 for female, 2 for male, 3 for nonbinary, 4 for other designations, etc.) These numbers simply represent information. They can be thought of as a placeholder for information. One is not better or worse than two. Using nominal measurements gives us a way to add information, in a simple way, to statistical software and be able to calculate how many of each kind are in our data.
Ordinal measurement puts data in order. In this case one may be better than two, but there are no measurable distinctions about how much better one is than two. An example of this could be a rating in a Likert Scale. (1. Strongly agree 2. Agree 3. Neutral 4. Somewhat disagree 5. Disagree). In this example #1 is clearly more agreeable than #2, but there is no way to measure it numerically. There is no way to calculate a value, but the numbers can be put into statistical software and they can be reported as more favorable or less favorable.
Interval measurements allow researchers to rank people and things at specific intervals. There are measurable values between the rankings (unlike the ordinal measurements). Some statistical procedures require that the level of measurement be at least interval. An example of an interval measurement is an IQ score. Between a score of 100 and 120 are 20 points, and there is the same distance between 120 and 140 – 20 points. The difference between two scores can be easily calculated and used in a statistical analysis.
Interval measurements do not have an unlimited range. A living person cannot have an IQ of zero, and they cannot have an IQ of 500. There is an upper limit to the range and an IQ does not have a zero level that is meaningful. Also, an IQ number could be affected by multiple variables. An IQ is measured from the person having taken a test. Did they have a migraine on testing day? Were they emotionally disturbed from a situation at home. There are lots of things that can affect an outcome. So, while it is a meaningful measurement, it is not a measurement that could be measured exactly again and again against a standard, like weight, height, etc. This makes interval measurements differ from Ratio measurements.
Ratio Measurement is the highest level of measurement. Unlike interval measurements, ratio measurements have a meaningful zero and can be expressed as ratios. An example is weight. A weight of zero is a meaningful measure, and we can also say that someone who weighs 200kg is 100% heavier than a person who weighs 100kg. So, a relationship between data points can be expressed and evaluated in several different ways.
For your discussion this week, please imagine and describe a work of research that you imagine. Write a hypothesis or a research question that adequately lets readers know what you would like to study. Create an imaginary population and setting and create a list of at least 6 points of data that you will hypothetically collect. Remember to include at least one dependent and independent variable, and other data points that can be categorized as nominal, ordinal, interval, or ratio. Do not use data points that are in the above example. Make sure to label your data points accurately.
Provide a substantive response to at least one classmate. Do you agree with the categorizations that they have given for their variables? Could their imagined work of research be potentially used as evidenced based practice? What kind of impact could your classmates’ research have on your practice as a nurse? Make sure that your responses are respectfully and professional.
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