Articulate meaning relevant to the main topic, scope, and purpose of the prompt.Competency 5: Address assignment purpose in a well-organized text, incorporating appropriate evidence and tone in grammatically sound sentences.Interpret the effect size for correlation analysis results.Competency 3: Interpret the results and practical significance of statistical health care data analyses.Perform an appropriate correlation test to determine the direction and strength or magnitude of the relationship between two variables.Perform a normal distribution assumption test for two variables to determine if data is normally distributed.Create a histogram and scatter plot for variables tested for normal distribution.Competency 2: Apply appropriate statistical methods using common software tools in the collection and evaluation of health care data.Interpret the overall clinical meaning and limitations of the relationship of two variables, based on a correlation analysis and literature regarding age and stress.Competency 1: Describe underlying concepts and reasoning related to the collection and evaluation of quantitative data in health care research.Demonstration of Proficiencyīy successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies. Correlational analyses are often later performed as part of the predetermined data analysis plan to answer a specific research question. With this approach, the researcher performs a very basic series of exploratory tests on variable pairs to identify any potentially interesting (yet unknown) relationships between groups of data (variables). One of these latter tests is a correlation analysis. Before weaving the strands of data into an analytical story that is related to a study’s goals, researchers typically inspect the completeness and quality of the data with various visualization techniques (graphics), summary tables, and mathematical tests of quality (assumption tests), as discussed in Assessment 2. One of the most important steps along the researcher’s path to data analysis is to become familiar with the character of the raw data collected for the project. This group is considered dependent because nothing is expected to vary in the nature of the individuals being measured except as a result of the intervention, as the group is composed of the same individuals. But because they are measured before and after an intervention, we consider them as two groups for analytical purposes. The dependent t-test, also called paired t-test, compares two groups for a dependent variable measured at the interval or ratio level as well however, these two groups are in reality just one group. For example, we may wish to compare a drug treatment group to a control group (those not receiving drug treatment) for a specific clinical characteristic (dependent variable) that can be measured at the interval or ratio level (such as cholesterol, depression scale, or memory test). The independent t-test, also called unpaired t-test, is typically used in health care to compare two groups of individuals that are entirely unrelated to each other (that is, independent), thus the one group cannot influence the other group. In this unit you will begin exploring popular statistical techniques (and their assumptions) that are used to compare two or more groups. In each of these examples we have two groups (two groups being compared or the same group being compared before and after), and one dependent variable that is being compared in each group. For example, for the dependent variable stress score, we may want to know if there is a difference in stress between males and females, or maybe we would like to know if there is a difference in stress levels between people who drink chamomile tea and those who do not, or maybe we would like to determine if a group of expectant parents is less anxious (this is the dependent variable) about the birthing experience after a series of discussions with experienced parents. In this unit we focus on whether two or more groups have important differences on a single variable of interest. For this three-part assessment you will create a histogram or bar graph for a data set, perform assumption and correlation tests, and interpret your graphic and test results in a 2-to-3 page paper.
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