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Chapter 1: Q6. (page 49)
Explain the difference between quantitative and qualitative data?
The main difference between quantitative and qualitative data is that the former is represented by numbers while later is represented by a name, code, or any other attribute such as a symbol.
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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–video 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. “These results,” say the researchers, “suggest that action video game players [and non-gamers] are equally susceptible to the costs of dividing attention in a complex task”.
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.
What is statistics?
The “lucky store effect” in lottery ticket sales. In the American Economic Review (Vol. 98, 2008), University of Chicago researchers investigated the lucky store effect theory in lottery ticket sales, i.e., the theory that a lottery retail store that sold a large-prize-winning ticket will experience greater ticket sales the following week. The researchers examined the weekly ticket sales of all 24,400 active lottery retailers in Texas. The analysis showed that “the week following the sale of [a winning Lotto Texas ticket], the winning store experiences a 12 to 38 percent relative sales increase. . . . ” Consequently, the researchers project that future winning lottery retail stores will experience the lucky store effect. Is this study an example of descriptive statistics or inferential statistics? Explain.
Drafting NFL quarterbacks. The National Football League (NFL) is a lucrative business, generating an annual revenue of about $8 million. One key to becoming a financially successful NFL team is drafting a good quarterback (QB) out of college. The NFL draft allows the worst-performing teams in the previous year the opportunity of selecting the best quarterbacks coming out of college. The Journal of Productivity Analysis (Vol. 35, 2011) published a study of how successful NFL teams are in drafting productive quarterbacks. Data were collected for all 331 quarterbacks drafted between 1970 and 2007. Several variables were measured for each QB, including draft position (one of the top 10 players picked, selection between picks 11 and 50, or selected after pick 50), NFL winning ratio (percentage of games won), and QB production score (higher scores indicate more productive QBs). The researchers discovered that draft position is only weakly related to a quarterback’s performance in the NFL. They concluded that “quarterbacks taken higher [in the draft] do not appear to perform any better.”
a. What is the experimental unit for this study?
b. Identify the type (quantitative or qualitative) of each variable measured.
c. Suppose you want to use this study to project the performance of future NFL QBs. Is this an application of descriptive or inferential statistics? Explain.
The economic return to earning an MBA. What are the economic rewards (e.g., higher salary) to obtaining an MBA degree? This was the question of interest in an article published in the International Economic Review (August 2008). The researchers made inferences based on wage data collected for a sample of 3,244 individuals who sat for the Graduate Management Admissions Test (GMAT). (The GMAT exam is required for entrance into most MBA programs.) The following sampling scheme was employed. All those who took the GMAT exam in any of four selected time periods were mailed a questionnaire. Those who responded to the questionnaire were then sent three follow-up surveys (one survey every 3 months). The final sample of 3,244 represents only those individuals who responded to all four surveys. (For example, about 5,600 took the GMAT in one time period; of these, only about 800 responded to all four surveys.)
A. For this study, describe the population of interest.
b. What method was used to collect the sample data?
c. Do you think the final sample is representative of the population? Why or why not? Comment on potential biases in the sample.
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