College application data. Colleges and universities are requiring an increasing amount of information about applicants before making acceptance and financial aid decisions. Classify each of the following types of data required on a college application as quantitative or qualitative.
a. High school GPA
b. Honors, awards
c. Applicant's score on the SAT or ACT
d. Gender of applicant
e. Parents’ income
f. Age of applicant
a. Quantitative Data
b. Qualitative Data
c. Quantitative Data
d. Qualitative Data
e. Quantitative Data
f. Quantitative Data
Quantitative data comprises all those values which are quantifiable. Therefore, one can measure them. The High school GPA takes on countable values such as 1, 2……9, 10. Thus, it is quantitative data.
Honors and awards are qualitative data since the categories of awards and honors can be defined. It could be the first prize, second prize and so on. Qualitative data depicts the type of award.
The SAT or ACT score is published in numeric’s. For instance, Mr. X has obtained 550 on 1000 in SAT, or Ms. Y has obtained 700 scores. These can take numerical values. Therefore, it is a part of quantitative data.
The qualitative data comprises the descriptive values and represents the types. For gender, there can be types such as female, male, and others.
The income of parents is measured in numerical worths like $10000, $11,000, and so on. This is quantitative data.
The data on the age of applicants is numeric. Hence, age is measured as 19 years, 20 years, and others. Therefore, it is quantitative data.
Stock prices. As part of an economics class project, students were asked to randomly select 500 New York Stock Exchange (NYSE) stocks from the Wall Street Journal. They were then asked to summarize the current prices (also referred to as the closing price of the stock for a particular trading date) of the collected stocks using graphical and numerical techniques.
a. Identify the population of interest for this study.
b. Is the variable being collected quantitative or qualitative?
c. Identify the data collection method used in this study.
d. Identify the sample of interest for this study.
e. Identify the experimental unit and variable of interest for this study.
Guilt in decision making. The effect of guilt emotion on how a decision maker focuses on the problem was investigated in the Journal of Behavioral Decision Making (January 2007). A total of 171 volunteer students participated in the experiment, where each was randomly assigned to one of three emotional states (guilt, anger, or neutral) through a reading/writing task. Immediately after the task, the students were presented with a decision problem (e.g., whether or not to spend money on repairing a very old car). The researchers found that a higher proportion of students in the guilty-state group chose to repair the car than those in the neutral-state and anger-state groups.
a. Identify the population, sample, and variables measured for this study.
b. Identify the data-collection method used.
c. What inference was made by the researcher?
d. In later chapters you will learn that the reliability of an inference is related to the size of the sample used. In addition to sample size, what factors might affect the reliability of the inference drawn in this study?
Random-digit dialing. To ascertain the effectiveness of their advertising campaigns, firms frequently conduct telephone interviews with consumers using random-digit dialing. With this approach, a random number generator mechanically creates the sample of phone numbers to be called. Each digit in the phone number is randomly selected from the possible digits 0, 1, 2, . . . , 9. Use the procedure to generate five seven-digit telephone numbers whose first three digits (area code) are 373.
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.
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