We have all heard about validity and reliability in research. Validity tells us that your results actually measure what you wanted to measure. Reliability means your results can be consistently reproduced. But before either of those two attributes of research can be considered, there is fidelity: Did you conduct your research as planned? In today's post, we'll explore the lesser-known member of this research quality triumvirate.
Not all scientific studies are created equal. Some studies are well-designed, with results that stand up to the intense scrutiny, analysis, and replication demanded by the scientific method. On the other hand, some studies are designed poorly, resulting in conclusions that can be called into question or that are not supported by the data. In this post, we explore two of the major ways that scientific studies are evaluated, giving you some tools to help in your own evaluation of the caliber of research studies.
The two aspects of research quality we will discuss today are internal validity and external validity. First, let’s consider the word validity. A study is considered valid - from the Latin word for 'strong' - if it is strongly supported by facts and logic. In terms of scientific research, to have valid conclusions, a study must have a valid design. This brings us to internal validity.
A young woman sits in a clinic halfway around the world, waiting for her COVID-19 vaccine. The entities involved in getting that vaccine to her, all the way from its development, manufacture, and distribution, relies on a massive global health network. In today's post, we'll highlight just some of those steps, learning about how global health is funded and implemented along the way.
What happens after laboratory tests confirm that an environmental product kills bacteria? Is that the end of the line for testing a product's efficacy? One pair of researchers say no. Here is their proposal for an evidence hierarchy that describes how, in theory, data can begin to connect a product to a reduction in HAIs. While many regulatory agencies exist to protect the consumer by ensuring that HAI reduction claims are true, it is important for us to still be aware of the burden of proof in research, and how that plays out in a laboratory and real-life setting.
The scientific method demands that researchers follow logical steps in their process to ensure that results are definitive. Without following these steps, including the proper design of experiments, the resulting data is not reliable. Over time, the research establishment has determined certain types of experimental designs, their advantages and disadvantages, as well as which type of design is appropriate for certain fields or contexts. Today we’ll get an overview of the types of experimental designs and how they impact the research conducted in healthcare infection control.
Recent news about a new variant of concern, Omicron, has dominated headlines. While we all try to keep up with the developing story, including how much of a threat this new variant presents, we are going to take some time to learn about the group tasked with keeping tabs on new variants and advising the world on how best to prepare for them.
Discussion of the reduction of microorganisms in healthcare settings will often include the data as “log reductions.” To those of us more accustomed to percentages, this can be confusing. Today's post will explain how to interpret these numbers and, we hope, help our readers better understand how they are used in scientific literature.