
We dedicate this one whole chapter for data prep. We walk you through the chronological steps to prepare your data before you run any major test to answer your research questions. In this chapter, we walk you through the processes needed to clean your dataset. We begin with identifying the monotonous responses. These are the ones that have no variations (more on this in the chapter). We also teach you how to deal with missing values and outliers, and of course, testing if the data is normally distributed. SPSS will run the tests you ask and give you outputs regardless of the state of the dataset. But, you need to be confidence to say that the outputs are correct. If the data is wrong, then the outputs are questionable. Afterall, if you put in diesel for your car that runs on a petrol, the engine most probably runs but you may not reach the destination (and a costly repair ensues).
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
