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Who is better at multi-tasking? In business, employees are often asked to perform a complex task when their attention is divided (i.e., multi-tasking). Human Factors (May 2014) published a study designed to determine whether video game players are better than non鈥搗ideo game players at multi-tasking. Each in a sample of 60 college students was classified as a video game player or a nonplayer. Participants entered a street crossing simulator and were asked to cross a busy street at an unsigned intersection. The simulator was designed to have cars traveling at various high rates of speed in both directions. During the crossing, the students also performed a memory task as a distraction. Two variables were measured for each student: (1) a street crossing performance score (measured out of 100 points) and (2) a memory task score (measured out of 20 points). The researchers found no differences in either the street crossing performance or memory task score of video game players and non-gamers. 鈥淭hese results,鈥� say the researchers, 鈥渟uggest that action video game players [and non-gamers] are equally susceptible to the costs of dividing attention in a complex task鈥�.

a. Identify the experimental unit for this study.

b. Identify the variables measured as quantitative or qualitative.

c. Is this an application of descriptive statistics or inferential statistics? Explain.

Step by step solution

01

Identifying the Experimental unit

The experimental unit mainly indicates the objects considered in the research process.In this experiment, the type of object is the students, as their scores are considered in the research process.

02

Categorizing the variables as qualitative and quantitative

The street crossing performance scores and memory task scores are quantitative because scores are always numerical. The scores can be used for mathematical and statistical analysis, and so they are quantitative and not qualitative.

03

Specifying the statistics study

The study involves analyzing the data via statistical calculations to conclude the relationship between the scores.As several statistical conclusions are drawn based on the scores, it is an application of inferential statistics.

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