Handling Data Quality Issues and the Methods Used in Research and Surveys

Marketing Research
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Marketing Research
The three options that are open to a researcher with respect to how to handle data quality issue
More often than not, researchers encounter nonresponses when conducting different types of surveys. In some cases, participants would give their responses, which depict a suspicious pattern. Both of these scenarios hamper the quality of a research finding because they portray a falsified image and impression of the sample and the data collected CITATION Bur16 \l 1033 (Burns, Week, & Bush, 2016). Hence, researchers need to follow a separate stage of data preparation phase during their marketing research procedures which examine participants’ answers in a dataset. In effect, the feedback and responses are meticulously evaluated for data quality problems before analysis. Some of the common aspects to look for in a suspicious pattern of data include the omission of some items, incomplete responses, and the element of ‘no opinion’ responses.
However, researchers have the following options to help them handle the above data quality problems. Firstly, in cases where a researcher encounters many egregious errors in the participants’ responses, they have the option of discarding the participant’s entire data row. The option helps the researcher to mitigate egregious errors in the final report caused by uninformed data CITATION Bur16 \l 1033 (Burns, Week, & Bush, 2016). Such data result in inconsistency and inaccuracy of research findings deeming the study substandard. Secondly, in cases where the errors are minimal and may not distort or skew the research findings, researchers have the option of leaving the entire statistics in the dataset. Such data can then be used or manipulated by subsequent researchers to suit specific requirements. Lastly, in situations where the findings contain valid and apparent-ridden responses, scientists may choose to set incorrect data items to missing or blank data. The researchers then proceed to use the valid data and responses in further analyses.
 The differences between probability samples and non-probability samples and their advantages and disadvantages
Probability and non-probability samples are the two main methods that are commonly used in research and surveys. In the former method, every person has an equal chance to be selected as a participant in the survey CITATION Bur16 \l 1033 (Burns, Week, & Bush, 2016). Therefore, there are known chances that members of the population will be selected to the sample. On the contrary, in non-probability sampling techniques, the probability of selecting respondents from the population to participate in the survey is not known. Researchers who use the non-probability samples may fail to determine the effect of sampling error and the size of the sample because the method lacks some sampling units.
Some of the advantages of probability sampling include the provision of a complete sampling structure to the researcher. This enables the researcher to access the broad elaborate contact information of the population sample in question CITATION Bur16 \l 1033 (Burns, Week, & Bush, 2016). The method also gives participants an equal opportunity to be selected for the survey. This allows for random sampling from the audience without neglecting some portions of the population. Additionally, this method enables researchers to generalize their findings using a random sample because of the even and unlimited selection of respondents. However, this method has the following disadvantages; it is comparatively more time-consuming and expensive than non-probability sampling methods. On the other hand, the advantages of non-probability sampling include; less costly and more convenient compared to probability sampling CITATION Bur16 \l 1033 (Burns, Week, & Bush, 2016). It is also efficient and effective when getting feedbacks and when generating ideas. Nonetheless, its disadvantages include; it does not allow the researcher to determine the effect of their sampl...