Data Measurement 2

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DataMeasurement 2

DataMeasurement 2

Someanalytical methods require variables, which meet certain minimummeasurement levels. A variable is defined as any quantity, which canbe measured. Additionally, it differs through a given population. Thevariables include qualitative, quantitative, independent anddependent (James, Witten, Tisbshirani, &amp Hastie, 2013). Inpractice, the emphasis is placed on the type of scale of measurementbecause there is a need to assess the appropriateness of thestatistical methods employed. Moreover, one must evaluate if theconclusions made are valid. This means that it is hard to tell if theoutcomes of a given research are credible without knowing the kind ofvariables and measures employed in acquiring the data. Thus, it isvital to plan the method of analysis accordingly for example, theordinary descriptive statistics utilized with nominal variables aremode and percentages. On the other hand, the inferential statisticsinvolve the chi-square.

Clearly,the level of a variable’s measurement is related directly to thedata set to be analyzed. Conventionally, the measurement level isused to refer to the type of variable. For examples, qualitativevariables go hand-in-hand with nominal and ordinal scales dependingon the kind of variable of interest. Thus, it is true to say thatstatistical computation is done with that specific variable in themeasurement. Single-item measures employ one question in measuringthe concept the researcher is interested in (James et al., 2013). Theimplication here is that the manner one responds to a single questioncan just be partially a reflection of the field of interest, therebyyielding flawed behavior or attitude indices. In order to overcomethis issue, the remedy is to utilize scales rather than single-itemquestions.


James,G., Witten, D., Tisbshirani, R., &amp Hastie, T. (2013). Anintroduction tostatistical

learning:With application in R. NewYork, NY: Springer