EDAPT EVALUATING DESCRIPTIVE DATA
Summarizing descriptive data is the first and most crucial step in the analysis of quantitative research data.
Evaluating research as evidence differs from the needs of those conducting research studies in descriptive
analysis. Descriptive statistics help provide basic information about variables in a dataset and highlight
relationships between variables.
In evidence-based practice, descriptive data is utilized before and after implementing an intervention or
practice change by selecting the most appropriate statistical technique. The statistical analysis and significance
of the outcomes should be reported in a comprehensive and meaningful way to inform decision-making in the
practice setting.
Click to specify which descriptions apply to descriptive statistics and which apply to inferential statistics.
Descriptive statistics give information about raw data, which describes the data in some manner, helps
organize, analyze, and present data in a meaningful way with the help of charts, graphs, tables, etc., and are
used to describe a situation.
Inferential statistics allow us to compare data, make hypotheses and predictions, make inferences about
populations using data drawn from the population, can be achieved by probability, attempt to draw
conclusions about a population, and are used to explain the chance of an occurrence of an event.
Using the drop-down menus, match the rule number to the correct description the nurse uses when reading
a study's results section.
Rule #1: Understand the purpose of the study.
Rule #2: Identify the variables—dependent and independent.
Rule #3: Identify how the variables are measured.
Rule #4: Look at the measures of central tendency and the measures of variability for the study variables.
The most common errors in summarized research data occur when inappropriate statistics are utilized.
Data results can be influenced by those who input the data.
Researchers should always disclose frequency and percentage in research reports.
A common error in nursing research is the over interpretation of descriptive results.
Graphical representation of data that hides aspects of the data set is a common pitfall in research.