These data can come from any part of the research process, including poor research design, inappropriate measurement materials, or flawed data entry.Ĭlean data meet some requirements for high quality while dirty data are flawed in one or more ways. clean dataĭirty data include inconsistencies and errors. Your organization decides to invest in this new drug and people are prescribed the drug instead of effective therapies. You conclude that the drug is effective when it’s not. Example: Type I errorBased on the results, you make a Type I error. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities. With inaccurate or invalid data, you might make a Type I or II error in your conclusion. If you forget to reverse code these answers before analysis, you may end up with an invalid conclusion because of data errors. Reverse coding means flipping the number scale in the opposite direction so that an extreme value (e.g., 1 or 7) means the same thing for each question. But the answers to negatively worded questions need to be reverse coded before analysis so that all answers are consistently in the same direction. Negative frame: I do not feel energetic after getting 8 hours of sleep at night.īoth questions measure the same thing: how respondents feel after waking up in the morning.Positive frame: I feel well rested when I wake up in the morning.Question: Please rate the extent to which you agree or disagree with these statements from 1 to 7. Example: Data errorsMost of the questions are framed positively, but some questions have negative frames to engage the participants. If you don’t remove or resolve these errors, you could end up with a false or invalid study conclusion. Using closed-ended questions, you ask Likert-scale questions about participants’ experiences and symptoms on a 1-to-7 scaleĮrrors are often inevitable, but cleansing your data helps you minimize them. You survey participants before and at the end of the drug treatment. Example: Quantitative researchYou investigate whether a new drug reduces the effects of fatigue. Improperly cleansed or calibrated data can lead to several types of research bias, particularly information bias and omitted variable bias. Using hypothesis testing, you find out whether your data demonstrate support for your research predictions. In quantitative research, you collect data and use statistical analyses to answer a research question. Frequently asked questions about data cleansing.
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