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Chapter 7: Problem 38

Refer to the instructions prior to this exercise. A study of adult Americans conducted by the polling organization Ipsos ("One in Five Americans Consider Themselves 'Entrepreneurs," November 7, 2016, www.ipsos-na.com /news- polls/pressrelease.aspx?id=7462, retrieved November 8,2016 ) asked each person in a sample whether he or she self-identified as an entrepreneur. The responses to this question were used to learn about the proportion of adult Americans who self-identify as an entrepreneur.

Short Answer

Expert verified
To calculate the proportion of adult Americans who self-identify as entrepreneurs based on the Ipsos poll results, first find the sample size (\(n\)) and the number of people self-identifying as entrepreneurs (\(x\)). Then, calculate the proportion by dividing \(x\) by \(n\), and express the proportion as a percentage by multiplying it by 100: \[Percentage = \frac{x}{n} \times 100\%\]

Step by step solution

01

Identify the given information

In this exercise, we are given the Ipsos poll results about adult Americans self-identifying as entrepreneurs.
02

Determine the sample size

To calculate the proportion of adult Americans who self-identify as entrepreneurs, we first need to know the sample size. Look at the polling data to find the number of people surveyed. Suppose the sample size is \(n\).
03

Calculate the number of people self-identifying as entrepreneurs

Now, find the number of people who self-identified as entrepreneurs from the poll data. Suppose there are \(x\) people who consider themselves entrepreneurs.
04

Calculate the proportion of adult Americans self-identifying as entrepreneurs

To calculate the proportion of people who consider themselves entrepreneurs, divide the number of people self-identifying as entrepreneurs (\(x\)) by the sample size (\(n\)): \(Proportion = \frac{x}{n}\)
05

Express the proportion as a percentage

To express the proportion as a percentage, multiply the proportion by 100: \[Percentage = Proportion \times 100\%\] Now, you can interpret the results as the percentage of adult Americans who self-identify as an entrepreneur based on the Ipsos poll.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Entrepreneurship Self-Identification
Entrepreneurship self-identification refers to the extent to which individuals see themselves as entrepreneurs. This concept goes beyond starting a business; it embodies a mindset that encompasses innovation, risk-taking, and a proactive approach to opportunities. When individuals are asked whether they perceive themselves as entrepreneurs, various factors influence their response, including cultural norms, personal experiences, and the broader socio-economic environment.

In educational settings, understanding how people self-identify in this way is crucial for tailoring entrepreneurial education programs. Aspiring entrepreneurs can benefit from learning spaces that reinforce their self-identification with resources, mentorship, and practical training. In the study mentioned, the polling organization Ipsos aimed to capture this self-perception among American adults to get a sense of the entrepreneurial spirit pervading society.
Polling Data Analysis
Polling data analysis is an essential tool used in a variety of fields including marketing, political science, and social research. It aims to understand the opinions, behaviors, and characteristics of a particular group of people by collecting data through surveys or questionnaires. Effective polling involves designing questions that accurately capture respondents' views, selecting a representative sample of the population, and using appropriate statistical methods to analyze the results.

For example, in the Ipsos study on entrepreneurship, a sample of adult Americans was questioned about their self-identification as entrepreneurs. Analyzing this data provides insights into how widespread entrepreneurial traits are in the population and can help in crafting policies or programs aimed at fostering entrepreneurial skills. It also illustrates the public's perception of entrepreneurship as a career choice, signaling potential shifts in the workforce landscape.
Statistical Proportion Calculation
The calculation of a statistical proportion is a fundamental aspect of data analysis, especially in survey research. It involves determining the ratio of a subset of the sample that possesses a particular characteristic to the total sample size. Proportions offer a clear way to understand the prevalence or commonality of a feature within a group.

To calculate the proportion of adult Americans who identify as entrepreneurs, as done in the Ipsos study, you divide the number of affirmative responses by the total number of surveyed participants. Expressing this data as a percentage makes it more relatable and easier to communicate. By expressing the results in such a way, stakeholders, including policy-makers, educators, and entrepreneurial organizations, can better gauge the impact of their initiatives on fostering entrepreneurship.

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Most popular questions from this chapter

Refer to the instructions prior to Exercise 7.22 . Researchers at the Medical College of Wisconsin studied 2121 children between the ages of 1 and 4 (Milwaukee Journal Sentinel, November 26,2005\()\). For each child in the study, a measure of iron deficiency and the length of time the child was bottle-fed were recorded. The resulting data were used to learn about whether there was a relationship between iron deficiency and the length of time a child is bottle fed.

Do people better remember what they learned if they are in the same physical space where they first learned it? The authors of the paper "The Dynamics of Memory: ContextDependent Updating" (Learning \& Memory (2008): \(574-579)\) asked people to learn a set of 20 unrelated objects. Two days later, these people were asked to recall the objects learned on the first day. Some of the people were asked to recall the objects in the same room where they originally learned the objects. The others were asked to recall the objects in a different room. People were assigned at random to one of these two recall conditions. The authors found that the data on the number of objects recalled supported the claim that recall is better when people return to the original learning context. Is the inference made one that involves estimation or one that involves hypothesis testing?

Suppose that a study was carried out in which each person in a random sample of students at a particular college was asked how much money he or she spent on textbooks for the current semester. Would you use these data to estimate a population mean or to estimate a population proportion? How did you decide?

Refer to the instructions prior to Exercise 7.25. Can moving their hands help children learn math? This question was investigated by the authors of the paper "Gesturing Gives Children New Ideas about Math" (Psychological Science [2009]: 267-272). A study was conducted to compare two different methods for teaching children how to solve math problems of the form \(3+2+8=8\). One method involved having students point to the \(3+2\) on the left side of the equal sign with one hand and then point to the blank on the right side of the equal sign before filling in the blank to complete the equation. The other method did not involve using these hand gestures. To compare the two methods, 128 children were assigned at random to one of the methods. Each child then took a test with six problems, and the number correct was determined for each child. The researchers planned to see if the resulting data supported the theory that the mean number correct for children who use hand gestures is higher than the mean number correct for children who do not use hand gestures.

Refer to the instructions prior to Exercise 7.25. An article in USA TODAY (October 19,2010 ) described a study to investigate how young children learn. Sixty-four toddlers age 18 months participated in the study. The toddlers were allowed to play in a lab equipped with toys and which had a robot that was hidden behind a screen. The article states: "After allowing the infants playtime, the team removed the screen and let the children see the robot. In some tests, an adult talked to the robot and played with it. In others the adult ignored the robot. After the adult left the room, the robot beeped and then turned its head to look at a toy to the side of the infant." The researchers planned to see if the resulting data supported the claim that children are more likely to follow the robot's gaze to the toy when they see an adult play with the robot than when they see an adult ignore the robot.
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