Create a baseline demographic table and a 2-to-3 page narrative summary.
Statistics is the art and science of data collection and interpretation. It is an art because it requires a combination of creativity, an eye for what makes sense, and personal judgment about how to use the end result. It is a science because it requires a systematic way of organizing, transforming, analyzing, describing, and interpreting data. Even if you dislike math, you can still enjoy statistics because it is not just about doing calculations or performing mathematical gymnastics. Most of the mathematics that makes learners uncomfortable is hidden inside statistical technological tools that we can use with relative ease in health care to make important discoveries. So relax—we are going to let technology do most of the work!
In this assessment, we focus on the cornerstone of quantitative research: the variable. One of the many things that makes the health care field so fascinating (and challenging) is the variation we find from one human to the next. Age, gender, eye color, heart rate, ethnicity, emotional response, and food preferences are some of the differences we find in our communities around the globe. In the language of statistics, each of these characteristics is called a variable. Some characteristics, like gender, have little variation, while other characteristics, like age, can have a much larger amount of variation.
Throughout this course, you will see that variables have special names based on their functional roles in the experiment. For example, when a variable is associated with the intervention (such as treatment, where we design the experiment to allow for only two options: practicing yoga versus not), it is referred to as an independent variable.
And when a variable is associated with an outcome in the experiment (for example, stress—which we decide, arbitrarily, will have only three possible levels: high, medium, and low) that is used to measure the direct consequences of the experimental treatment, we refer to this as a dependent variable.
The sneaky thing about statistics is that depending on the circumstances, the independent variable is often referred to in other terms, such as the controlled, explanatory, or predictor variable. If you consider this briefly, the names make sense because you are controlling who gets which treatment, where the treatment really is the key factor in explaining (or predicting) any outcome. The dependent variable may also be referred to as response, outcome, output, or experimental variable. In this course, we will try to be fairly consistent, using the terms independent and dependent.
The baseline demographic table plays an important role in reporting study results. It summarizes key characteristics of participants numerically (such as age, gender, and ethnicity) at the beginning of a study, before any intervention takes place. Baseline demographic tables are often among the first tables found in the results section of capstone papers, dissertations, and peer-reviewed publications as well. For this assessment, you will create a baseline demographic table and narrative summary using the linked Resources.
By successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies.
The following statistical analysis software is required to complete your assessments in this course:
You have access to the more robust IBM SPSS Statistics Premium GradPack.
Please refer to the Statistical Software page on Campus for general information on SPSS software, including the most recent version made available to Capella learners.
Please review the scoring guide before submitting your assessment. The requirements outlined above correspond to the grading criteria in the scoring guide, so be sure to address each point. In addition, you may choose to review the performance-level descriptions for each criterion to see how your work will be assessed.
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