There are many elements involved in survey design that affect the quality of data that comes out of a survey – the time and effort it takes for survey respondents to complete a survey, order of questions, number of points on the rating scale, order of question-answer options – to name a few. In other words, data that are unreliable, irreproducible, or invalid, leading to the wrong conclusions and actions. Conversely, bad surveys produce flawed data. Good surveys produce accurate data and critical information, providing salient windows into the core of the topic under exploration. Given the wide range of options in conducting a survey, it is imperative for the consumer/reader of survey findings to understand the potential for bias as well as the strategies and techniques used for reducing bias, so that appropriate conclusions can be drawn from the data. The term “survey” reflects a range of research objectives – target populations and sampling frames, recruitment strategies, survey instrument designs, survey administration methods, data processing, and statistical adjustment –to ensure a high-quality survey process and outcome. In other words, data that is unreliable, irreproducible, or invalid, leading to the wrong conclusions and actions. What this implies is that there are good surveys, and there are bad surveys. Particularly when stakes are high, we need to be sure that we are gathering the right data. Have you found yourself in a discussion about the “reliability” or “validity” of data that comes out of surveys such as course evaluations or employee engagement surveys? Why does the reliability or validity of survey data matter?įlawed data can lead to wrong conclusions. We stand behind many causes that enrich our communities – near and far. We strive to provide an experience that is human-centered and meaningful. We empower our people to bring achievement into their own lives.

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