![\"Systematic](\"http:\/\/wp.lancs.ac.uk\/language-literacy\/files\/2022\/03\/SR_BlogPost_Image1_Stages-300x85.png\")
Systematic Review Image 1<\/p><\/div>\n
Keep reading if you\u2019re interested in exactly how systematic reviews reduce bias and how conducting one may be beneficial to you!<\/p>\n
Systematic reviews minimise author bias<\/u><\/p>\n
Author bias refers to when an author shows favouritism towards certain evidence (whether intentionally or unintentionally) and threatens the trustworthiness of a review. Whilst I\u2019m sure that most researchers set out to be unbiased when selecting research to include in their review, it is almost impossible to be aware of every piece of evidence relevant to a particular research area.<\/p>\n
Fortunately, a systematic search will reduce the impact of author bias on your review by attempting to find all of the available evidence on your chosen research question. Tip:<\/strong> for best results, I suggest consulting with an expert to help you produce your search, such as a librarian trained to conduct systematic reviews.<\/p>\n Systematic reviews consider bias in individual studies<\/u><\/p>\n Another source of bias originates from your included studies because a review is only as trustworthy as the research included within it. This is why a good systematic review includes a quality assessment that highlights high-quality research and alerts the reader to studies that may be influenced by some bias. For example, sampling bias, whereby the participants recruited for the study do not accurately represent the study\u2019s target population.<\/p>\n Unfortunately, a tool that specifically addresses the quality of non-medical research doesn\u2019t currently exist. This means that researchers are forced to edit existing quality assessment tools to suit their purposes (for example, the Downs and Black checklist<\/a>).<\/p>\n Luckily, the NIROES collaboration<\/a> hopes to solve this problem by creating a quality assessment tool specific to non-medical research. Such a tool is incredibly important for building trust in the quality of psychological research.<\/p>\n Systematic reviews minimise publication bias<\/u><\/p>\n An additional source of bias that can affect the quality of your review is publication bias. Publication bias refers to the fact that journals are much more likely to publish papers with positive findings compared to research that doesn\u2019t find any statistically significant effects. This can seriously affect how you interpret the findings of your included studies.<\/p>\n Tips:<\/strong> to combat publication bias, researchers are encouraged to search for relevant grey literature (for example, pre-prints, dissertations, and theses) using online platforms like PsyArXiv<\/a>. Authors should also conduct a funnel plot, which will provide you with a visual representation of how much publication bias has affected your research area of interest.<\/p>\n An asymmetrical funnel plot indicates the presence of publication bias. Funnel Plot<\/p><\/div>\n Besides reducing bias, systematic reviews are beneficial because they encourage open and transparent research practices. For example, many journals will now only publish your systematic review if you have pre-registered it.<\/p>\n Pre-registering involves making your hypotheses, methodology, and statistical analyses publically available on online platforms, such as the Open Science Framework<\/a>, before you begin conducting your review. Tip:<\/strong> the NIRO-SR Checklist<\/a> provides clear guidance for pre-registering your review and encourages you to be transparent about your decisions.<\/p>\n Systematic reviews help identify gaps in the research<\/u><\/p>\n Another advantage of systematic reviews is that they will help you (and your readers!) identify new research questions that haven\u2019t been addressed by the current literature. Systematic reviews can also help you to identify limitations of the research in your area of interest (for example, methodological issues and common causes of bias) that you could address in a future study.<\/p>\n Concluding remarks<\/u><\/p>\n
\nStudy precision: standard error or sample size.<\/p>\n![\"Funnel](\"http:\/\/wp.lancs.ac.uk\/language-literacy\/files\/2022\/03\/SR_BlogPost_Image2_Funnel-300x193.png\")
\nSystematic reviews encourage reproducibility<\/u><\/p>\n