Frequency and Descriptive Statistics Imagine that you have collected data from 100 patients. You have carefully compiled vitals, pain scores, and medications for each of the patients. However, what does all of this data mean? Is your work now done? How do we make data meaningful? Why must we move beyond the raw data to ensure that data is purposeful? Descriptive analysis is the analysis of the data to develop meaning. Descriptive analysis provides meaning through showing, describing, and summarizing the data compiled to reveal characteristics of the sample and to describe study variables (Gray & Grove, 2020). This allows the researcher to present data in a more meaningful and simplified way. For this Assignment, summarize your interpretation of the descriptive statistics provided to you in the Week 4 Descriptive Statistics SPSS Output document. You will evaluate each variable in your analysis. To Prepare: Review the 4 Descriptive Statistics SPSS Output provided (Will attach) Review how to interpret descriptive statistics, including how to interpret research outcomes. Consider the results presented in the SPSS output and reflect on how you might interpret the frequency distributions and the descriptive statistics presented. The Assignment: (3 pages) Summarize your interpretation of the frequency data provided in the output for respondents age, highest school grade completed, and family income from prior month. Note: A frequency analysis is way of summarizing data by depicting the number of times a data value occurs in the data table or output. It is used to analyze the data set including where the data are concentrated or clustered, the range of values, observation of extreme values, and to determine intervals for analysis that could make sense in categorizing your variable values. Summarize your interpretation of the descriptive statistics provided in the output for respondents age, highest school grade completed, race and ethnicity, currently employed, and family income from prior month. Note: The descriptive analysis includes N (size of your sample), the mean, the median, the standard deviation, the size and spread of your data to determine the variability/variance in your data.
