You learn about statistical conclusion errors in every basic nursing research class and are expected to understand what these errors mean. Wait! Did I learn about this, you ask? Yes, yes, you did. Remember Type I and Type II errors? Those are statistical conclusion errors. But I’ll tell you that
P-values and Confidence Intervals In a previous post, I talked about p-values – what they tell you and what they don’t tell you. Quick recap since talking about confidence intervals is related to p-values: p-values ONLY tell you whether a research result is statistically significant or not based on the
This month’s blog theme is nursing research, so I’m discussing the meaning of some statistical concepts to help you interpret the research studies you are reading. I’m going to talk about several concepts this month that I have found both undergraduate and graduate students struggle to really understand. Last week
There are two types of significance used to interpret research studies – statistical significance and clinical significance. They are not the same thing. One answers the question, Are the statistical results due to random chance? and the other answers the question, So what? Will the results matter to our patients?
At some point in your graduate nursing program, you will most likely have to write a literature review about an assigned topic or a self-selected topic (e.g., your thesis, dissertation, or capstone project topic). Undergraduate students don’t typically have to write a full-blown literature review; however, the tips in this post
This post will explain how to use a theory to frame a research study. General recommendations that can be used for any research will be shared. Theories, Frameworks, and Models – Oh My! Frameworks to guide research can be based on philosophies, theories, or conceptual models. Every research study has
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This podcast discusses the data quality concepts of reliability and validity of research tools/instruments. Definitions and examples of methods of reliability (stability, internal consistency, equivalence measures) and validity testing (content validity, concurrent and predictive validity, criterion-related validity, convergent and divergent validity, mixed method/multimethod approach, hypothesis testing, factor analysis) and how
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This podcast provides an introduction to data quality, which emphasizes the importance of reliability and validity of research tools and instruments. The nature of data, forms of data, and a brief overview of data measurement are presented, including a description of random and systematic error. Click on the Podcast Player to get
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This episode defines the differences between qualitative research designs and mixed methods designs. Trustworthiness (reliability and validity) of qualitative designs is also discussed. Oops! To gain access to free or premium content, you’ll need to be a registered Member! If you are already logged in (that is, if you
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