“I want RESULTS!”

(Analysis of Data to Answer the Hypothesis or Question)

The “Results” section of a research article tells you both 1) the characteristics of those who participated in the study (e.g., their gender, ethnicity, education, & so on); & 2) the data analysis used to answer the research question or hypothesis.   The Results section follows the methods section that is described in preceding blogs.

“Results” is where the author reports analysis of numbers data using statistics or analysis of word data by identifying common themes.  Don’t be afraid to read this section; & don’t let your eyes glaze over.   All this comes with practice.  Here are a few basics to get started.*

1st          The researcher has collected data (or measurements) about something in numbers (e.g., inches or test scores) or words (e.g., subjects’ descriptions of experiences) or data in both numbers and words.  The researcher will analyze numbers data using statistical tests and will analyze word data for recurring themes and ideas.  

2nd        The characteristics of the participants in the study will tell you whether the participants are similar to or different from those to whom you want to apply the results.

3rd         In statistical analysis when you see that a result is p<.05 this means that there is a 95% chance the result is right and a 5% chance it is wrong.  When you see p<.01, it means that there is a 99% chance the result is right and a 1% chance the result is wrong.

4th         In statistics the researcher will analyze number data to do at least one of these:

a.       Describe something (for example, What are RNs’ self-care practices);

b.      Find out whether two things are related to each other (for example, Is maternal age related to numbers of birth defects); or 

c.       Identify whether one thing is causing another (for example, Does ZMapp vaccine cause those with Ebola virus to get well).  

5th         Not surprisingly, statistical analysis that describes something is called descriptive statistics (4^th.a above). Examples are percents & averages.  In contrast, statistical analyses done to find relationships or cause and effect are called inferential statistics (4^th b.c. above).  Examples are correlation coefficients or t-tests.

6th          In word analysis, the researcher will be able only to describe something (e.g., what do RNs experience when returning to school).

*[For more on basic statistics see Halfens, R.J.G., & Meijers, J.M.M. (2013). Back to basics: An introduction to statistics. Journal of Wound Care, 22(5): 248-51.]

CRITICAL THINKING

1.       Is the following example, did Zerwekh et al do word analysis or statistical analysis?  Did they describe, correlate, or explain cause and effect? EXAMPLE: In Zerwekh et al (2002) study, analysis showed that barriers to pain management came from 5 sources: “within the patient, within the physician, within the family, within the nurse and within the healthcare organization” (p.85).  [Hint: see 1^st & 6^th above]

2.       In the following example, were Smith et al (2010) focusing on identifying relationships or cause and effect?  How might you describe the likelihood that they were right, based on the “p” levels?  EXAMPLE: “Better pain management was associated with increased emotional well-being (t = 2.11, p = 0.03). Number of hospitalizations was marginally associated with increased emotional well-being (t = 1.91, p < 0.06).” (p.83)